5th Assignment
Kijun Kwon
2018-12-20
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stringr)
library(tm)## Loading required package: NLPlibrary(wordcloud)## Loading required package: RColorBrewerlibrary(plyr)## -------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## -------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarizelibrary(arules)## Loading required package: Matrix##
## Attaching package: 'arules'## The following object is masked from 'package:tm':
##
## inspect## The following object is masked from 'package:dplyr':
##
## recode## The following objects are masked from 'package:base':
##
## abbreviate, writelibrary(arulesViz)## Loading required package: gridlibrary(igraph)##
## Attaching package: 'igraph'## The following object is masked from 'package:arules':
##
## union## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## unionQ1)
papers <- read.csv("4th Assignment Text Mining 2013170833.csv")
p_num <- nrow(papers)
p_num## [1] 2027Q2)
years <- NULL
for(i in 1:p_num){
tmp_years <- lapply(strsplit(as.String(papers$meta[i]), ' ', fixed = T),'[',4) %>% unlist
years <- c(years, tmp_years)
}
years_tab <- table(years)
barplot(years_tab ,main='Submission Years',xlab='Years group',ylab='Proportion')
pie(years_tab,main='Submission Years')
mosaicplot(years_tab ,main='Submission Years',xlab='Years group')
Q3)
num_auth <- 0
for(i in 1:p_num){
tmp_num <- length(strsplit(as.String(papers$author[i]), ',', fixed = T) %>% unlist)
if(num_auth<tmp_num){
num_row <- i
num_auth <- tmp_num
}
}
papers$title[num_row]## [1] Deep Learning for Quality Control of Subcortical Brain 3D Shape Models
## 2026 Levels: #phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter ...strsplit(as.String(papers$author[num_row]), ',', fixed = T)## [[1]]
## [1] "Dmitry Petrov" " Boris A. Gutman Egor Kuznetsov"
## [3] " Theo G.M. van Erp" " Jessica A. Turner"
## [5] " Lianne Schmaal" " Dick Veltman"
## [7] " Lei Wang" " Kathryn Alpert"
## [9] " Dmitry Isaev" " Artemis Zavaliangos-Petropulu"
## [11] " Christopher R.K. Ching" " Vince Calhoun"
## [13] " David Glahn" " Theodore D. Satterthwaite"
## [15] " Ole Andreas Andreassen" " Stefan Borgwardt"
## [17] " Fleur Howells" " Nynke Groenewold"
## [19] " Aristotle Voineskos" " Joaquim Radua"
## [21] " Steven G. Potkin" " Benedicto Crespo-Facorro"
## [23] " Diana Tordesillas-Gutierrez" " Li Shen"
## [25] " Irina Lebedeva" " Gianfranco Spalletta"
## [27] " Gary Donohoe" " Peter Kochunov"
## [29] " Pedro G.P. Rosa" " Anthony James"
## [31] " Udo Dannlowski" " Bernhard T. Baune"
## [33] " Andre Aleman" " Ian H. Gotlib"
## [35] " Henrik Walter" " Martin Walter"
## [37] " Jair C. Soares" " Stefan Ehrlich"
## [39] " Ruben C. Gur" " N. Trung Doan"
## [41] " Ingrid Agartz" " Lars T. Westlye"
## [43] " Fabienne Harrisberger" " Anita Riecher-Rossler"
## [45] " Anne Uhlmann" " Dan J. Stein"
## [47] " Erin W. Dickie" " Edith Pomarol-Clotet"
## [49] " Paola Fuentes-Claramonte" " Erick Jorge Canales-Rodriguez"
## [51] " Raymond Salvador" " Alexander J. Huang"
## [53] " Roberto Roiz-Santianez" " Shan Cong"
## [55] " Alexander Tomyshev" " Fabrizio Piras"
## [57] " Daniela Vecchio" " Nerisa Banaj"
## [59] " Valentina Ciullo" " Elliot Hong"
## [61] " Geraldo Busatto" " Marcus V. Zanetti"
## [63] " Mauricio H. Serpa" " Simon Cervenka"
## [65] " Sinead Kelly" " Dominik Grotegerd"
## [67] " Matthew D. Sacchet" " Ilya M. Veer"
## [69] " Meng Li" " Mon-Ju Wu"
## [71] " Benson Irungu" " Esther Walton"
## [73] " Paul M. Thompson"Q4)
len_title <- 0
for(i in 1:p_num){
tmp_num <- nchar(as.String(papers$title[i]))
if(len_title<tmp_num){
num_row <- i
len_title <- tmp_num
}
}
papers$title[num_row]## [1] Phase 4: DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real-Time Recognition and Localization of Marine Mammals - Distributed Processing and Big Data Applications
## 2026 Levels: #phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter ...Q5)
years_list <- strsplit(levels(factor(years)), ' ', fixed = T)
len_title <- NULL
len_abs <- NULL
num_auth <- NULL
for(i in years_list){
count <- 0
tmp_len_title <- 0
tmp_len_abs <- 0
tmp_num_auth <- 0
for(j in 1:p_num){
if(years[j] == i){
count <- count + 1
tmp_len_title <- tmp_len_title + nchar(as.String(papers$title[j]))
tmp_len_abs <- tmp_len_abs + nchar(as.String(papers$abstract[j]))
tmp_num_auth <- tmp_num_auth + length(strsplit(as.String(papers$author[j]), ',', fixed = T) %>% unlist)
}
}
len_title <- c(len_title, tmp_len_title/count)
len_abs <- c(len_abs, tmp_len_abs/count)
num_auth <- c(num_auth, tmp_num_auth/count)
}
data.frame(len_title, len_abs, num_auth, row.names = years_list)## len_title len_abs num_auth
## 2011 41.00000 1296.0000 2.000000
## 2013 55.73333 800.6667 2.400000
## 2014 63.20000 1035.9333 3.233333
## 2015 71.08824 1100.3922 3.921569
## 2016 72.35865 1136.5485 4.050633
## 2017 75.74920 1125.7428 4.070740
## 2018 77.00000 1161.1892 4.714706idx_list <- list()
for(i in years_list){
tmp <- which(years==i)
idx_list[length(idx_list)+1] <- list(tmp)
}
years_list[length(years_list)+1] <- list("all")
idx_list[length(idx_list)+1] <- list(1:length(years))
names(idx_list) <- years_list
abs_list <- NULL
for(i in 1:length(years_list)){
abs_tmp <- as.data.frame(papers$abstract[idx_list[i] %>% unlist])
abs_list <- c(abs_list, abs_tmp)
}
# Construct a list of corpuses
corp_list <- list()
for(i in 1:length(years_list)){
corp_tmp <- Corpus(VectorSource(as.factor(abs_list[i] %>% unlist)))
corp_list[i] <- list(corp_tmp)
}
# Data preprocessing
# deep learning으로 검색하였으므로, redundancy를 예상하여 "deep"과 "learning"은 제외했다.
myStopwords <- c(stopwords("SMART"), "deep", "learning")
prep_list <- list()
for(i in 1:length(years_list)){
prep_temp <- tm_map(corp_list[[i]], content_transformer(tolower))
prep_temp <- tm_map(prep_temp, content_transformer(removePunctuation))
prep_temp <- tm_map(prep_temp, content_transformer(removeNumbers))
prep_temp <- tm_map(prep_temp, removeWords, myStopwords)
prep_temp <- tm_map(prep_temp, stemDocument)
prep_list[i] <- list(prep_temp)
}## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents# Construct Term-Document Matrix, Word cloud
tdm_list <- list()
wc_list <- list()
for(i in 1:length(years_list)){
tmp_tdm <- TermDocumentMatrix(prep_list[[i]], control = list(minWordLength = 1))
tdm_list[i] <- list(tmp_tdm)
tmp_wc <- as.matrix(tmp_tdm)
wc_list[i] <- list(tmp_wc)
}Q6)
sparsity <- c(0, 89, 93, 97, 98, 99, 99)
dat <- data.frame(years_list[1:7] %>% unlist, sparsity, stringsAsFactors=FALSE)
dat## years_list.1.7......unlist sparsity
## 1 2011 0
## 2 2013 89
## 3 2014 93
## 4 2015 97
## 5 2016 98
## 6 2017 99
## 7 2018 99Q7)
word_freq <- sort(rowSums(wc_list[[length(wc_list)]]), decreasing=TRUE)
word_freq[1:50]## network model imag data train method
## 3025 2553 2051 2033 1819 1757
## neural propos perform approach result system
## 1740 1511 1408 1212 1171 1068
## predict featur dataset algorithm base paper
## 997 981 970 938 933 922
## show comput convolut detect problem task
## 888 871 823 810 743 735
## classif accuraci architectur applic framework improv
## 718 690 682 676 637 632
## achiev work present machin demonstr process
## 622 618 614 602 601 585
## high inform set techniqu challeng time
## 570 564 558 557 556 542
## develop compar studi recent learn segment
## 538 525 512 504 495 481
## provid optim
## 475 474Q8)
wf_list <- list()
for(i in 1:(length(years_list))){
wd_tmp <- sort(rowSums(wc_list[[i]]), decreasing=TRUE)
wf_list[i] <- list(wd_tmp)
}
for(i in 1:(length(years_list)-1)){
barplot(wf_list[[i]][1:8], main='Frequent Words',xlab='Words',ylab='Frequency')
}
network 어는 항상 가장 많이 쓰이는 단어였다.(2011년은 표본이 1개라 제외) 과거에 비해 ‘model’, ‘data’, ’image’의 사용 빈도가 늘어나고 있으며, ’represent’는 사용빈도가 줄어들고 있다.
Q9)
for(i in 1:length(years_list)){
name_tmp <- names(wf_list[[i]])
wcd_tmp <- data.frame(word=name_tmp, freq=wf_list[[i]])
pal <- brewer.pal(8, "Dark2")
wordcloud(wcd_tmp$word, wcd_tmp$freq, min.freq=300, scale = c(3, 0.1),
rot.per = 0.1, col=pal, random.order=F)
}
image라는 단어가 최근에 많이 등장했는데, 최근 산업에서 deep learning이 시각 인식에 많이 접목됨을 추측할 있다.
Q10)
for(i in 4:length(years_list)){
tmp_wcm <- wc_list[[i]]
tmp_wcm[tmp_wcm >= 1] <- 1
freq_idx1 <- which(rowSums(tmp_wcm) > length(idx_list[[i]])/3)
freq_wcmat1 <- wc_list[[i]][freq_idx1,]
# Transform into a term-term adjacency matrix
termMatrix1 <- freq_wcmat1 %*% t(freq_wcmat1)
# inspect terms numbered 5 to 10
termMatrix1[1:10,1:10]
g1 <- graph.adjacency(termMatrix1, weighted=T, mode = "undirected")
g1 <- simplify(g1)
V(g1)$label <- V(g1)$name
V(g1)$degree <- degree(g1)
g1 <- delete.edges(g1, which(E(g1)$weight <= 3))
set.seed(3952)
layout1 <- layout.fruchterman.reingold(g1)
# Make the network look better
V(g1)$label.cex <- V(g1)$degree/max(V(g1)$degree)
V(g1)$label.color <- rgb(0, 0, 0.2, 0.8)
V(g1)$frame.color <- NA
egam1 <- 3*(log(E(g1)$weight+1))/max(log(E(g1)$weight+1))
E(g1)$color <- rgb(0.5, 0.5, 0)
E(g1)$width <- egam1
# plot the graph in layout1
plot(g1, layout=layout.kamada.kawai)
}
’perform’과 ’train’의 밀접한 관계를 볼 수 있다. train에 따라 신경망 model의 성능이 결정되기 때문이다. (2013년도까지는 표본이 적어서 임의로 제외하였음.)
Q11)
insp_list <- NULL
for(i in 3:length(years_list)){
tmp_tran <- as.matrix(t(tdm_list[[i]]))
tmp_tran <- as(tmp_tran, "transactions")
tmp_rules <- apriori(tmp_tran, parameter=list(minlen=2,supp=0.1, conf=0.7))
insp_list[i] <- list(inspect(tmp_rules)[1:10,])
}## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 3
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[846 item(s), 30 transaction(s)] done [0.00s].
## sorting and recoding items ... [170 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 done [0.00s].
## writing ... [28995 rule(s)] done [0.01s].
## creating S4 object ... done [0.04s].
## lhs rhs support confidence lift count
## [1] {prior} => {represent} 0.1000000 1.0000000 2.000000 3
## [2] {prior} => {model} 0.1000000 1.0000000 1.875000 3
## [3] {shown} => {problem} 0.1000000 1.0000000 3.333333 3
## [4] {shown} => {method} 0.1000000 1.0000000 2.727273 3
## [5] {shown} => {show} 0.1000000 1.0000000 1.875000 3
## [6] {convent} => {problem} 0.1000000 1.0000000 3.333333 3
## [7] {convent} => {method} 0.1000000 1.0000000 2.727273 3
## [8] {convent} => {show} 0.1000000 1.0000000 1.875000 3
## [9] {current} => {perform} 0.1000000 1.0000000 2.142857 3
## [10] {current} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11] {current} => {featur} 0.1000000 1.0000000 1.875000 3
## [12] {infer} => {make} 0.1000000 1.0000000 3.333333 3
## [13] {infer} => {paper} 0.1000000 1.0000000 3.000000 3
## [14] {infer} => {data} 0.1000000 1.0000000 2.307692 3
## [15] {infer} => {model} 0.1000000 1.0000000 1.875000 3
## [16] {artifici} => {machin} 0.1000000 1.0000000 4.285714 3
## [17] {human} => {propos} 0.1000000 1.0000000 2.000000 3
## [18] {human} => {featur} 0.1000000 1.0000000 1.875000 3
## [19] {bayesian} => {applic} 0.1000000 1.0000000 4.285714 3
## [20] {bayesian} => {perform} 0.1000000 1.0000000 2.142857 3
## [21] {obtain} => {propos} 0.1000000 1.0000000 2.000000 3
## [22] {address} => {problem} 0.1000000 1.0000000 3.333333 3
## [23] {address} => {perform} 0.1000000 1.0000000 2.142857 3
## [24] {memori} => {reduc} 0.1000000 1.0000000 4.285714 3
## [25] {memori} => {network} 0.1000000 1.0000000 1.578947 3
## [26] {main} => {make} 0.1000000 1.0000000 3.333333 3
## [27] {search} => {recent} 0.1000000 1.0000000 4.285714 3
## [28] {code} => {task} 0.1000000 1.0000000 2.727273 3
## [29] {larger} => {result} 0.1000000 1.0000000 3.000000 3
## [30] {larger} => {approach} 0.1000000 1.0000000 2.500000 3
## [31] {larger} => {dataset} 0.1000000 1.0000000 2.307692 3
## [32] {larger} => {propos} 0.1000000 1.0000000 2.000000 3
## [33] {larger} => {model} 0.1000000 1.0000000 1.875000 3
## [34] {label} => {task} 0.1000000 1.0000000 2.727273 3
## [35] {label} => {data} 0.1000000 1.0000000 2.307692 3
## [36] {label} => {featur} 0.1000000 1.0000000 1.875000 3
## [37] {ident} => {face} 0.1000000 1.0000000 7.500000 3
## [38] {face} => {ident} 0.1000000 0.7500000 7.500000 3
## [39] {ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [40] {ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [41] {ident} => {train} 0.1000000 1.0000000 2.500000 3
## [42] {ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [43] {standard} => {make} 0.1000000 1.0000000 3.333333 3
## [44] {standard} => {problem} 0.1000000 1.0000000 3.333333 3
## [45] {standard} => {perform} 0.1000000 1.0000000 2.142857 3
## [46] {standard} => {model} 0.1000000 1.0000000 1.875000 3
## [47] {pose} => {dataset} 0.1000000 1.0000000 2.307692 3
## [48] {pose} => {propos} 0.1000000 1.0000000 2.000000 3
## [49] {pose} => {featur} 0.1000000 1.0000000 1.875000 3
## [50] {tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [51] {tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [52] {tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [53] {tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [54] {tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [55] {factor} => {model} 0.1000000 0.7500000 1.406250 3
## [56] {spars} => {perform} 0.1000000 1.0000000 2.142857 3
## [57] {spars} => {represent} 0.1000000 1.0000000 2.000000 3
## [58] {spars} => {propos} 0.1000000 1.0000000 2.000000 3
## [59] {oper} => {task} 0.1000000 1.0000000 2.727273 3
## [60] {oper} => {data} 0.1000000 1.0000000 2.307692 3
## [61] {discrimin} => {perform} 0.1000000 1.0000000 2.142857 3
## [62] {discrimin} => {learn} 0.1000000 1.0000000 2.307692 3
## [63] {discrimin} => {model} 0.1000000 1.0000000 1.875000 3
## [64] {discrimin} => {featur} 0.1000000 1.0000000 1.875000 3
## [65] {construct} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [66] {construct} => {show} 0.1000000 0.7500000 1.406250 3
## [67] {construct} => {model} 0.1000000 0.7500000 1.406250 3
## [68] {advantag} => {classif} 0.1000000 1.0000000 3.750000 3
## [69] {advantag} => {method} 0.1000000 1.0000000 2.727273 3
## [70] {advantag} => {approach} 0.1000000 1.0000000 2.500000 3
## [71] {advantag} => {featur} 0.1000000 1.0000000 1.875000 3
## [72] {advantag} => {network} 0.1000000 1.0000000 1.578947 3
## [73] {perceptron} => {paper} 0.1000000 1.0000000 3.000000 3
## [74] {perceptron} => {train} 0.1000000 1.0000000 2.500000 3
## [75] {difficult} => {model} 0.1000000 1.0000000 1.875000 3
## [76] {equival} => {complex} 0.1000000 1.0000000 5.000000 3
## [77] {equival} => {featur} 0.1000000 1.0000000 1.875000 3
## [78] {initi} => {layer} 0.1000000 1.0000000 5.000000 3
## [79] {initi} => {work} 0.1000000 1.0000000 2.500000 3
## [80] {initi} => {network} 0.1000000 1.0000000 1.578947 3
## [81] {nonlinear} => {power} 0.1000000 1.0000000 7.500000 3
## [82] {power} => {nonlinear} 0.1000000 0.7500000 7.500000 3
## [83] {nonlinear} => {perform} 0.1000000 1.0000000 2.142857 3
## [84] {nonlinear} => {featur} 0.1000000 1.0000000 1.875000 3
## [85] {finetun} => {framework} 0.1000000 1.0000000 5.000000 3
## [86] {finetun} => {train} 0.1000000 1.0000000 2.500000 3
## [87] {finetun} => {work} 0.1000000 1.0000000 2.500000 3
## [88] {singl} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [89] {singl} => {achiev} 0.1000000 0.7500000 3.214286 3
## [90] {singl} => {network} 0.1000000 0.7500000 1.184211 3
## [91] {local} => {imag} 0.1000000 1.0000000 6.000000 3
## [92] {local} => {perform} 0.1000000 1.0000000 2.142857 3
## [93] {local} => {show} 0.1000000 1.0000000 1.875000 3
## [94] {local} => {propos} 0.1000000 1.0000000 2.000000 3
## [95] {speech} => {layer} 0.1000000 1.0000000 5.000000 3
## [96] {speech} => {result} 0.1000000 1.0000000 3.000000 3
## [97] {previous} => {network} 0.1000000 1.0000000 1.578947 3
## [98] {field} => {machin} 0.1000000 1.0000000 4.285714 3
## [99] {field} => {classif} 0.1000000 1.0000000 3.750000 3
## [100] {field} => {learn} 0.1000000 1.0000000 2.307692 3
## [101] {field} => {featur} 0.1000000 1.0000000 1.875000 3
## [102] {engin} => {result} 0.1000000 1.0000000 3.000000 3
## [103] {engin} => {represent} 0.1000000 1.0000000 2.000000 3
## [104] {engin} => {featur} 0.1000000 1.0000000 1.875000 3
## [105] {easili} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [106] {easili} => {task} 0.1000000 1.0000000 2.727273 3
## [107] {easili} => {data} 0.1000000 1.0000000 2.307692 3
## [108] {form} => {input} 0.1000000 0.7500000 3.214286 3
## [109] {form} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [110] {form} => {network} 0.1000000 0.7500000 1.184211 3
## [111] {pretrain} => {show} 0.1000000 1.0000000 1.875000 3
## [112] {under} => {present} 0.1000000 0.7500000 4.500000 3
## [113] {under} => {method} 0.1000000 0.7500000 2.045455 3
## [114] {under} => {train} 0.1000000 0.7500000 1.875000 3
## [115] {under} => {data} 0.1000000 0.7500000 1.730769 3
## [116] {under} => {show} 0.1333333 1.0000000 1.875000 4
## [117] {under} => {model} 0.1000000 0.7500000 1.406250 3
## [118] {transfer} => {task} 0.1000000 1.0000000 2.727273 3
## [119] {transfer} => {data} 0.1000000 1.0000000 2.307692 3
## [120] {transfer} => {network} 0.1000000 1.0000000 1.578947 3
## [121] {scheme} => {comput} 0.1000000 1.0000000 4.285714 3
## [122] {great} => {task} 0.1000000 1.0000000 2.727273 3
## [123] {great} => {data} 0.1000000 1.0000000 2.307692 3
## [124] {condit} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [125] {condit} => {train} 0.1000000 1.0000000 2.500000 3
## [126] {condit} => {propos} 0.1000000 1.0000000 2.000000 3
## [127] {common} => {approach} 0.1000000 1.0000000 2.500000 3
## [128] {common} => {dataset} 0.1000000 1.0000000 2.307692 3
## [129] {common} => {show} 0.1000000 1.0000000 1.875000 3
## [130] {common} => {propos} 0.1000000 1.0000000 2.000000 3
## [131] {handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [132] {handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [133] {handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [134] {handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [135] {handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [136] {handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [137] {includ} => {network} 0.1000000 1.0000000 1.578947 3
## [138] {compon} => {experi} 0.1000000 1.0000000 3.750000 3
## [139] {compon} => {dataset} 0.1000000 1.0000000 2.307692 3
## [140] {compon} => {propos} 0.1000000 1.0000000 2.000000 3
## [141] {compon} => {model} 0.1000000 1.0000000 1.875000 3
## [142] {provid} => {neural} 0.1000000 1.0000000 3.000000 3
## [143] {provid} => {train} 0.1000000 1.0000000 2.500000 3
## [144] {provid} => {work} 0.1000000 1.0000000 2.500000 3
## [145] {provid} => {dataset} 0.1000000 1.0000000 2.307692 3
## [146] {provid} => {network} 0.1000000 1.0000000 1.578947 3
## [147] {multipl} => {task} 0.1000000 1.0000000 2.727273 3
## [148] {multipl} => {data} 0.1000000 1.0000000 2.307692 3
## [149] {test} => {object} 0.1000000 1.0000000 3.750000 3
## [150] {test} => {classif} 0.1000000 1.0000000 3.750000 3
## [151] {test} => {propos} 0.1000000 1.0000000 2.000000 3
## [152] {test} => {model} 0.1000000 1.0000000 1.875000 3
## [153] {test} => {featur} 0.1000000 1.0000000 1.875000 3
## [154] {extract} => {general} 0.1000000 0.7500000 3.750000 3
## [155] {extract} => {recognit} 0.1000000 0.7500000 2.500000 3
## [156] {extract} => {result} 0.1333333 1.0000000 3.000000 4
## [157] {extract} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [158] {extract} => {data} 0.1000000 0.7500000 1.730769 3
## [159] {extract} => {show} 0.1333333 1.0000000 1.875000 4
## [160] {extract} => {model} 0.1000000 0.7500000 1.406250 3
## [161] {extract} => {featur} 0.1000000 0.7500000 1.406250 3
## [162] {report} => {appli} 0.1000000 0.7500000 3.750000 3
## [163] {report} => {result} 0.1000000 0.7500000 2.250000 3
## [164] {report} => {work} 0.1000000 0.7500000 1.875000 3
## [165] {report} => {dataset} 0.1000000 0.7500000 1.730769 3
## [166] {report} => {propos} 0.1000000 0.7500000 1.500000 3
## [167] {report} => {network} 0.1333333 1.0000000 1.578947 4
## [168] {attribut} => {outperform} 0.1000000 1.0000000 7.500000 3
## [169] {outperform} => {attribut} 0.1000000 0.7500000 7.500000 3
## [170] {attribut} => {represent} 0.1000000 1.0000000 2.000000 3
## [171] {attribut} => {show} 0.1000000 1.0000000 1.875000 3
## [172] {employ} => {task} 0.1000000 1.0000000 2.727273 3
## [173] {employ} => {data} 0.1000000 1.0000000 2.307692 3
## [174] {employ} => {model} 0.1000000 1.0000000 1.875000 3
## [175] {employ} => {featur} 0.1000000 1.0000000 1.875000 3
## [176] {abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [177] {abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [178] {abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [179] {abil} => {network} 0.1000000 1.0000000 1.578947 3
## [180] {structur} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [181] {structur} => {perform} 0.1000000 0.7500000 1.607143 3
## [182] {structur} => {data} 0.1000000 0.7500000 1.730769 3
## [183] {structur} => {learn} 0.1000000 0.7500000 1.730769 3
## [184] {structur} => {show} 0.1333333 1.0000000 1.875000 4
## [185] {structur} => {model} 0.1000000 0.7500000 1.406250 3
## [186] {structur} => {featur} 0.1000000 0.7500000 1.406250 3
## [187] {inform} => {learn} 0.1000000 1.0000000 2.307692 3
## [188] {inform} => {show} 0.1000000 1.0000000 1.875000 3
## [189] {inform} => {propos} 0.1000000 1.0000000 2.000000 3
## [190] {inform} => {model} 0.1000000 1.0000000 1.875000 3
## [191] {convolut} => {effici} 0.1000000 0.7500000 4.500000 3
## [192] {convolut} => {reduc} 0.1000000 0.7500000 3.214286 3
## [193] {convolut} => {architectur} 0.1000000 0.7500000 2.812500 3
## [194] {convolut} => {work} 0.1000000 0.7500000 1.875000 3
## [195] {convolut} => {perform} 0.1000000 0.7500000 1.607143 3
## [196] {convolut} => {dataset} 0.1000000 0.7500000 1.730769 3
## [197] {convolut} => {network} 0.1333333 1.0000000 1.578947 4
## [198] {filter} => {propos} 0.1000000 1.0000000 2.000000 3
## [199] {highlevel} => {larg} 0.1000000 1.0000000 6.000000 3
## [200] {highlevel} => {work} 0.1000000 1.0000000 2.500000 3
## [201] {highlevel} => {represent} 0.1000000 1.0000000 2.000000 3
## [202] {care} => {design} 0.1000000 1.0000000 7.500000 3
## [203] {design} => {care} 0.1000000 0.7500000 7.500000 3
## [204] {care} => {task} 0.1000000 1.0000000 2.727273 3
## [205] {care} => {data} 0.1000000 1.0000000 2.307692 3
## [206] {care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [207] {care} => {learn} 0.1000000 1.0000000 2.307692 3
## [208] {care} => {featur} 0.1000000 1.0000000 1.875000 3
## [209] {care} => {network} 0.1000000 1.0000000 1.578947 3
## [210] {rate} => {success} 0.1000000 0.7500000 2.812500 3
## [211] {rate} => {dataset} 0.1000000 0.7500000 1.730769 3
## [212] {rate} => {learn} 0.1000000 0.7500000 1.730769 3
## [213] {rate} => {represent} 0.1000000 0.7500000 1.500000 3
## [214] {rate} => {show} 0.1000000 0.7500000 1.406250 3
## [215] {rate} => {network} 0.1000000 0.7500000 1.184211 3
## [216] {captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [217] {captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [218] {captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [219] {captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [220] {captur} => {network} 0.1000000 1.0000000 1.578947 3
## [221] {paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [222] {paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [223] {paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [224] {paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [225] {time} => {reduc} 0.1000000 0.7500000 3.214286 3
## [226] {time} => {show} 0.1000000 0.7500000 1.406250 3
## [227] {time} => {model} 0.1333333 1.0000000 1.875000 4
## [228] {time} => {network} 0.1000000 0.7500000 1.184211 3
## [229] {encod} => {work} 0.1000000 0.7500000 1.875000 3
## [230] {simpl} => {studi} 0.1000000 1.0000000 7.500000 3
## [231] {studi} => {simpl} 0.1000000 0.7500000 7.500000 3
## [232] {simpl} => {process} 0.1000000 1.0000000 5.000000 3
## [233] {simpl} => {work} 0.1000000 1.0000000 2.500000 3
## [234] {simpl} => {network} 0.1000000 1.0000000 1.578947 3
## [235] {baselin} => {classif} 0.1000000 1.0000000 3.750000 3
## [236] {baselin} => {propos} 0.1000000 1.0000000 2.000000 3
## [237] {increas} => {result} 0.1000000 0.7500000 2.250000 3
## [238] {increas} => {neural} 0.1000000 0.7500000 2.250000 3
## [239] {increas} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [240] {increas} => {train} 0.1000000 0.7500000 1.875000 3
## [241] {increas} => {dataset} 0.1000000 0.7500000 1.730769 3
## [242] {increas} => {represent} 0.1000000 0.7500000 1.500000 3
## [243] {increas} => {featur} 0.1000000 0.7500000 1.406250 3
## [244] {increas} => {network} 0.1333333 1.0000000 1.578947 4
## [245] {boltzmann} => {restrict} 0.1333333 1.0000000 7.500000 4
## [246] {restrict} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [247] {boltzmann} => {recent} 0.1000000 0.7500000 3.214286 3
## [248] {boltzmann} => {machin} 0.1333333 1.0000000 4.285714 4
## [249] {boltzmann} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [250] {boltzmann} => {task} 0.1000000 0.7500000 2.045455 3
## [251] {boltzmann} => {train} 0.1000000 0.7500000 1.875000 3
## [252] {boltzmann} => {data} 0.1000000 0.7500000 1.730769 3
## [253] {boltzmann} => {show} 0.1333333 1.0000000 1.875000 4
## [254] {boltzmann} => {model} 0.1333333 1.0000000 1.875000 4
## [255] {boltzmann} => {featur} 0.1000000 0.7500000 1.406250 3
## [256] {creat} => {model} 0.1000000 0.7500000 1.406250 3
## [257] {creat} => {network} 0.1333333 1.0000000 1.578947 4
## [258] {restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [259] {restrict} => {machin} 0.1333333 1.0000000 4.285714 4
## [260] {restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [261] {restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [262] {restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [263] {restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [264] {restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [265] {restrict} => {model} 0.1333333 1.0000000 1.875000 4
## [266] {restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [267] {extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [268] {extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [269] {extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [270] {extens} => {model} 0.1000000 1.0000000 1.875000 3
## [271] {present} => {network} 0.1333333 0.8000000 1.263158 4
## [272] {linear} => {framework} 0.1000000 0.7500000 3.750000 3
## [273] {linear} => {perform} 0.1000000 0.7500000 1.607143 3
## [274] {order} => {neural} 0.1000000 0.7500000 2.250000 3
## [275] {order} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [276] {order} => {approach} 0.1000000 0.7500000 1.875000 3
## [277] {order} => {show} 0.1000000 0.7500000 1.406250 3
## [278] {order} => {propos} 0.1000000 0.7500000 1.500000 3
## [279] {order} => {model} 0.1333333 1.0000000 1.875000 4
## [280] {order} => {featur} 0.1000000 0.7500000 1.406250 3
## [281] {order} => {network} 0.1000000 0.7500000 1.184211 3
## [282] {power} => {specif} 0.1000000 0.7500000 4.500000 3
## [283] {power} => {improv} 0.1000000 0.7500000 2.500000 3
## [284] {power} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [285] {power} => {train} 0.1000000 0.7500000 1.875000 3
## [286] {power} => {perform} 0.1000000 0.7500000 1.607143 3
## [287] {power} => {dataset} 0.1000000 0.7500000 1.730769 3
## [288] {power} => {model} 0.1000000 0.7500000 1.406250 3
## [289] {power} => {featur} 0.1000000 0.7500000 1.406250 3
## [290] {benchmark} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [291] {benchmark} => {classif} 0.1000000 0.7500000 2.812500 3
## [292] {benchmark} => {problem} 0.1000000 0.7500000 2.500000 3
## [293] {benchmark} => {perform} 0.1000000 0.7500000 1.607143 3
## [294] {benchmark} => {dataset} 0.1000000 0.7500000 1.730769 3
## [295] {benchmark} => {learn} 0.1333333 1.0000000 2.307692 4
## [296] {benchmark} => {propos} 0.1000000 0.7500000 1.500000 3
## [297] {benchmark} => {model} 0.1000000 0.7500000 1.406250 3
## [298] {benchmark} => {featur} 0.1333333 1.0000000 1.875000 4
## [299] {high} => {success} 0.1000000 0.7500000 2.812500 3
## [300] {high} => {object} 0.1000000 0.7500000 2.812500 3
## [301] {high} => {classif} 0.1000000 0.7500000 2.812500 3
## [302] {high} => {task} 0.1000000 0.7500000 2.045455 3
## [303] {high} => {learn} 0.1000000 0.7500000 1.730769 3
## [304] {high} => {represent} 0.1000000 0.7500000 1.500000 3
## [305] {high} => {propos} 0.1333333 1.0000000 2.000000 4
## [306] {high} => {featur} 0.1333333 1.0000000 1.875000 4
## [307] {parallel} => {framework} 0.1000000 0.7500000 3.750000 3
## [308] {parallel} => {comput} 0.1000000 0.7500000 3.214286 3
## [309] {parallel} => {reduc} 0.1000000 0.7500000 3.214286 3
## [310] {parallel} => {work} 0.1000000 0.7500000 1.875000 3
## [311] {parallel} => {network} 0.1333333 1.0000000 1.578947 4
## [312] {predict} => {outperform} 0.1000000 0.7500000 5.625000 3
## [313] {outperform} => {predict} 0.1000000 0.7500000 5.625000 3
## [314] {predict} => {challeng} 0.1000000 0.7500000 4.500000 3
## [315] {predict} => {paper} 0.1000000 0.7500000 2.250000 3
## [316] {predict} => {train} 0.1333333 1.0000000 2.500000 4
## [317] {predict} => {perform} 0.1000000 0.7500000 1.607143 3
## [318] {predict} => {data} 0.1000000 0.7500000 1.730769 3
## [319] {predict} => {represent} 0.1000000 0.7500000 1.500000 3
## [320] {predict} => {show} 0.1000000 0.7500000 1.406250 3
## [321] {predict} => {propos} 0.1000000 0.7500000 1.500000 3
## [322] {predict} => {model} 0.1000000 0.7500000 1.406250 3
## [323] {predict} => {featur} 0.1000000 0.7500000 1.406250 3
## [324] {number} => {model} 0.1333333 0.8000000 1.500000 4
## [325] {face} => {variat} 0.1000000 0.7500000 5.625000 3
## [326] {variat} => {face} 0.1000000 0.7500000 5.625000 3
## [327] {face} => {challeng} 0.1000000 0.7500000 4.500000 3
## [328] {face} => {imag} 0.1000000 0.7500000 4.500000 3
## [329] {face} => {recognit} 0.1333333 1.0000000 3.333333 4
## [330] {face} => {train} 0.1000000 0.7500000 1.875000 3
## [331] {face} => {data} 0.1000000 0.7500000 1.730769 3
## [332] {face} => {dataset} 0.1000000 0.7500000 1.730769 3
## [333] {face} => {learn} 0.1000000 0.7500000 1.730769 3
## [334] {face} => {represent} 0.1000000 0.7500000 1.500000 3
## [335] {face} => {propos} 0.1000000 0.7500000 1.500000 3
## [336] {face} => {featur} 0.1000000 0.7500000 1.406250 3
## [337] {sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [338] {sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [339] {sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [340] {sourc} => {learn} 0.1333333 1.0000000 2.307692 4
## [341] {sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [342] {sourc} => {show} 0.1333333 1.0000000 1.875000 4
## [343] {sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [344] {sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [345] {variat} => {general} 0.1000000 0.7500000 3.750000 3
## [346] {variat} => {recognit} 0.1333333 1.0000000 3.333333 4
## [347] {variat} => {task} 0.1000000 0.7500000 2.045455 3
## [348] {variat} => {data} 0.1000000 0.7500000 1.730769 3
## [349] {variat} => {learn} 0.1000000 0.7500000 1.730769 3
## [350] {variat} => {represent} 0.1000000 0.7500000 1.500000 3
## [351] {variat} => {show} 0.1000000 0.7500000 1.406250 3
## [352] {variat} => {featur} 0.1000000 0.7500000 1.406250 3
## [353] {introduc} => {process} 0.1000000 0.7500000 3.750000 3
## [354] {introduc} => {task} 0.1000000 0.7500000 2.045455 3
## [355] {introduc} => {data} 0.1000000 0.7500000 1.730769 3
## [356] {introduc} => {learn} 0.1000000 0.7500000 1.730769 3
## [357] {introduc} => {model} 0.1000000 0.7500000 1.406250 3
## [358] {introduc} => {featur} 0.1000000 0.7500000 1.406250 3
## [359] {introduc} => {network} 0.1000000 0.7500000 1.184211 3
## [360] {import} => {task} 0.1333333 1.0000000 2.727273 4
## [361] {import} => {approach} 0.1000000 0.7500000 1.875000 3
## [362] {import} => {data} 0.1333333 1.0000000 2.307692 4
## [363] {import} => {learn} 0.1000000 0.7500000 1.730769 3
## [364] {import} => {represent} 0.1333333 1.0000000 2.000000 4
## [365] {import} => {show} 0.1000000 0.7500000 1.406250 3
## [366] {import} => {model} 0.1333333 1.0000000 1.875000 4
## [367] {import} => {featur} 0.1333333 1.0000000 1.875000 4
## [368] {art} => {state} 0.1333333 1.0000000 7.500000 4
## [369] {state} => {art} 0.1333333 1.0000000 7.500000 4
## [370] {art} => {problem} 0.1000000 0.7500000 2.500000 3
## [371] {art} => {task} 0.1000000 0.7500000 2.045455 3
## [372] {art} => {perform} 0.1000000 0.7500000 1.607143 3
## [373] {art} => {data} 0.1000000 0.7500000 1.730769 3
## [374] {art} => {dataset} 0.1000000 0.7500000 1.730769 3
## [375] {art} => {learn} 0.1333333 1.0000000 2.307692 4
## [376] {art} => {propos} 0.1333333 1.0000000 2.000000 4
## [377] {art} => {model} 0.1333333 1.0000000 1.875000 4
## [378] {art} => {featur} 0.1000000 0.7500000 1.406250 3
## [379] {state} => {problem} 0.1000000 0.7500000 2.500000 3
## [380] {state} => {task} 0.1000000 0.7500000 2.045455 3
## [381] {state} => {perform} 0.1000000 0.7500000 1.607143 3
## [382] {state} => {data} 0.1000000 0.7500000 1.730769 3
## [383] {state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [384] {state} => {learn} 0.1333333 1.0000000 2.307692 4
## [385] {state} => {propos} 0.1333333 1.0000000 2.000000 4
## [386] {state} => {model} 0.1333333 1.0000000 1.875000 4
## [387] {state} => {featur} 0.1000000 0.7500000 1.406250 3
## [388] {joint} => {general} 0.1000000 0.7500000 3.750000 3
## [389] {joint} => {object} 0.1000000 0.7500000 2.812500 3
## [390] {joint} => {method} 0.1000000 0.7500000 2.045455 3
## [391] {joint} => {approach} 0.1000000 0.7500000 1.875000 3
## [392] {joint} => {perform} 0.1000000 0.7500000 1.607143 3
## [393] {joint} => {dataset} 0.1000000 0.7500000 1.730769 3
## [394] {joint} => {represent} 0.1000000 0.7500000 1.500000 3
## [395] {joint} => {show} 0.1333333 1.0000000 1.875000 4
## [396] {joint} => {propos} 0.1333333 1.0000000 2.000000 4
## [397] {joint} => {model} 0.1000000 0.7500000 1.406250 3
## [398] {studi} => {addit} 0.1000000 0.7500000 4.500000 3
## [399] {studi} => {imag} 0.1000000 0.7500000 4.500000 3
## [400] {studi} => {process} 0.1000000 0.7500000 3.750000 3
## [401] {studi} => {classif} 0.1000000 0.7500000 2.812500 3
## [402] {studi} => {work} 0.1000000 0.7500000 1.875000 3
## [403] {studi} => {learn} 0.1000000 0.7500000 1.730769 3
## [404] {studi} => {show} 0.1000000 0.7500000 1.406250 3
## [405] {studi} => {propos} 0.1000000 0.7500000 1.500000 3
## [406] {studi} => {featur} 0.1000000 0.7500000 1.406250 3
## [407] {studi} => {network} 0.1000000 0.7500000 1.184211 3
## [408] {evalu} => {paper} 0.1333333 0.8000000 2.400000 4
## [409] {evalu} => {method} 0.1333333 0.8000000 2.181818 4
## [410] {evalu} => {represent} 0.1333333 0.8000000 1.600000 4
## [411] {evalu} => {show} 0.1333333 0.8000000 1.500000 4
## [412] {evalu} => {model} 0.1333333 0.8000000 1.500000 4
## [413] {evalu} => {network} 0.1333333 0.8000000 1.263158 4
## [414] {outperform} => {challeng} 0.1000000 0.7500000 4.500000 3
## [415] {outperform} => {object} 0.1000000 0.7500000 2.812500 3
## [416] {outperform} => {task} 0.1000000 0.7500000 2.045455 3
## [417] {outperform} => {train} 0.1000000 0.7500000 1.875000 3
## [418] {outperform} => {data} 0.1000000 0.7500000 1.730769 3
## [419] {outperform} => {represent} 0.1000000 0.7500000 1.500000 3
## [420] {outperform} => {show} 0.1333333 1.0000000 1.875000 4
## [421] {outperform} => {propos} 0.1000000 0.7500000 1.500000 3
## [422] {outperform} => {model} 0.1000000 0.7500000 1.406250 3
## [423] {outperform} => {featur} 0.1000000 0.7500000 1.406250 3
## [424] {design} => {task} 0.1000000 0.7500000 2.045455 3
## [425] {design} => {work} 0.1000000 0.7500000 1.875000 3
## [426] {design} => {data} 0.1000000 0.7500000 1.730769 3
## [427] {design} => {dataset} 0.1000000 0.7500000 1.730769 3
## [428] {design} => {learn} 0.1000000 0.7500000 1.730769 3
## [429] {design} => {featur} 0.1000000 0.7500000 1.406250 3
## [430] {design} => {network} 0.1333333 1.0000000 1.578947 4
## [431] {system} => {perform} 0.1333333 0.8000000 1.714286 4
## [432] {system} => {dataset} 0.1333333 0.8000000 1.846154 4
## [433] {system} => {propos} 0.1333333 0.8000000 1.600000 4
## [434] {system} => {model} 0.1333333 0.8000000 1.500000 4
## [435] {system} => {featur} 0.1333333 0.8000000 1.500000 4
## [436] {detect} => {method} 0.1333333 0.8000000 2.181818 4
## [437] {detect} => {perform} 0.1333333 0.8000000 1.714286 4
## [438] {detect} => {dataset} 0.1333333 0.8000000 1.846154 4
## [439] {detect} => {propos} 0.1333333 0.8000000 1.600000 4
## [440] {detect} => {featur} 0.1666667 1.0000000 1.875000 5
## [441] {detect} => {network} 0.1333333 0.8000000 1.263158 4
## [442] {stateoftheart} => {propos} 0.1666667 1.0000000 2.000000 5
## [443] {effici} => {architectur} 0.1333333 0.8000000 3.000000 4
## [444] {effici} => {work} 0.1333333 0.8000000 2.000000 4
## [445] {effici} => {network} 0.1333333 0.8000000 1.263158 4
## [446] {set} => {addit} 0.1000000 0.7500000 4.500000 3
## [447] {set} => {process} 0.1000000 0.7500000 3.750000 3
## [448] {set} => {architectur} 0.1000000 0.7500000 2.812500 3
## [449] {set} => {recognit} 0.1000000 0.7500000 2.500000 3
## [450] {set} => {task} 0.1000000 0.7500000 2.045455 3
## [451] {set} => {work} 0.1000000 0.7500000 1.875000 3
## [452] {set} => {data} 0.1000000 0.7500000 1.730769 3
## [453] {set} => {dataset} 0.1000000 0.7500000 1.730769 3
## [454] {set} => {learn} 0.1000000 0.7500000 1.730769 3
## [455] {set} => {show} 0.1000000 0.7500000 1.406250 3
## [456] {set} => {propos} 0.1000000 0.7500000 1.500000 3
## [457] {set} => {network} 0.1000000 0.7500000 1.184211 3
## [458] {shallow} => {neural} 0.1333333 0.8000000 2.400000 4
## [459] {shallow} => {featur} 0.1333333 0.8000000 1.500000 4
## [460] {shallow} => {network} 0.1333333 0.8000000 1.263158 4
## [461] {semant} => {work} 0.1333333 0.8000000 2.000000 4
## [462] {semant} => {represent} 0.1666667 1.0000000 2.000000 5
## [463] {semant} => {show} 0.1333333 0.8000000 1.500000 4
## [464] {semant} => {propos} 0.1333333 0.8000000 1.600000 4
## [465] {analysi} => {process} 0.1000000 0.7500000 3.750000 3
## [466] {analysi} => {experi} 0.1000000 0.7500000 2.812500 3
## [467] {analysi} => {architectur} 0.1333333 1.0000000 3.750000 4
## [468] {analysi} => {classif} 0.1000000 0.7500000 2.812500 3
## [469] {analysi} => {neural} 0.1000000 0.7500000 2.250000 3
## [470] {analysi} => {train} 0.1000000 0.7500000 1.875000 3
## [471] {analysi} => {work} 0.1000000 0.7500000 1.875000 3
## [472] {analysi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [473] {analysi} => {propos} 0.1000000 0.7500000 1.500000 3
## [474] {analysi} => {featur} 0.1000000 0.7500000 1.406250 3
## [475] {analysi} => {network} 0.1333333 1.0000000 1.578947 4
## [476] {addit} => {appli} 0.1333333 0.8000000 4.000000 4
## [477] {addit} => {work} 0.1333333 0.8000000 2.000000 4
## [478] {addit} => {dataset} 0.1333333 0.8000000 1.846154 4
## [479] {addit} => {propos} 0.1666667 1.0000000 2.000000 5
## [480] {addit} => {network} 0.1333333 0.8000000 1.263158 4
## [481] {function} => {optim} 0.1333333 0.8000000 3.428571 4
## [482] {function} => {object} 0.1333333 0.8000000 3.000000 4
## [483] {function} => {problem} 0.1333333 0.8000000 2.666667 4
## [484] {function} => {show} 0.1333333 0.8000000 1.500000 4
## [485] {function} => {model} 0.1333333 0.8000000 1.500000 4
## [486] {function} => {featur} 0.1333333 0.8000000 1.500000 4
## [487] {accuraci} => {featur} 0.1666667 0.8333333 1.562500 5
## [488] {base} => {neural} 0.1333333 0.8000000 2.400000 4
## [489] {base} => {network} 0.1333333 0.8000000 1.263158 4
## [490] {potenti} => {result} 0.1333333 0.8000000 2.400000 4
## [491] {potenti} => {data} 0.1333333 0.8000000 1.846154 4
## [492] {potenti} => {dataset} 0.1333333 0.8000000 1.846154 4
## [493] {potenti} => {network} 0.1333333 0.8000000 1.263158 4
## [494] {specif} => {result} 0.1333333 0.8000000 2.400000 4
## [495] {specif} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [496] {specif} => {train} 0.1666667 1.0000000 2.500000 5
## [497] {specif} => {network} 0.1333333 0.8000000 1.263158 4
## [498] {techniqu} => {result} 0.1333333 0.8000000 2.400000 4
## [499] {techniqu} => {featur} 0.1333333 0.8000000 1.500000 4
## [500] {appli} => {perform} 0.1666667 0.8333333 1.785714 5
## [501] {appli} => {propos} 0.2000000 1.0000000 2.000000 6
## [502] {larg} => {work} 0.1666667 1.0000000 2.500000 5
## [503] {larg} => {represent} 0.1333333 0.8000000 1.600000 4
## [504] {challeng} => {train} 0.1333333 0.8000000 2.000000 4
## [505] {challeng} => {data} 0.1333333 0.8000000 1.846154 4
## [506] {challeng} => {learn} 0.1333333 0.8000000 1.846154 4
## [507] {challeng} => {represent} 0.1333333 0.8000000 1.600000 4
## [508] {challeng} => {show} 0.1333333 0.8000000 1.500000 4
## [509] {challeng} => {propos} 0.1333333 0.8000000 1.600000 4
## [510] {challeng} => {featur} 0.1333333 0.8000000 1.500000 4
## [511] {imag} => {recognit} 0.1333333 0.8000000 2.666667 4
## [512] {imag} => {perform} 0.1333333 0.8000000 1.714286 4
## [513] {imag} => {propos} 0.1666667 1.0000000 2.000000 5
## [514] {exist} => {work} 0.1333333 0.8000000 2.000000 4
## [515] {exist} => {model} 0.1333333 0.8000000 1.500000 4
## [516] {exist} => {network} 0.1333333 0.8000000 1.263158 4
## [517] {layer} => {result} 0.1666667 0.8333333 2.500000 5
## [518] {layer} => {algorithm} 0.1666667 0.8333333 2.083333 5
## [519] {complex} => {represent} 0.1666667 0.8333333 1.666667 5
## [520] {complex} => {show} 0.1666667 0.8333333 1.562500 5
## [521] {complex} => {featur} 0.1666667 0.8333333 1.562500 5
## [522] {general} => {show} 0.1666667 0.8333333 1.562500 5
## [523] {effect} => {paper} 0.1666667 0.7142857 2.142857 5
## [524] {effect} => {learn} 0.1666667 0.7142857 1.648352 5
## [525] {effect} => {represent} 0.1666667 0.7142857 1.428571 5
## [526] {effect} => {show} 0.1666667 0.7142857 1.339286 5
## [527] {applic} => {perform} 0.2000000 0.8571429 1.836735 6
## [528] {applic} => {data} 0.1666667 0.7142857 1.648352 5
## [529] {applic} => {represent} 0.2000000 0.8571429 1.714286 6
## [530] {applic} => {propos} 0.1666667 0.7142857 1.428571 5
## [531] {input} => {approach} 0.1666667 0.7142857 1.785714 5
## [532] {input} => {represent} 0.1666667 0.7142857 1.428571 5
## [533] {input} => {show} 0.1666667 0.7142857 1.339286 5
## [534] {input} => {model} 0.1666667 0.7142857 1.339286 5
## [535] {comput} => {network} 0.1666667 0.7142857 1.127820 5
## [536] {recent} => {machin} 0.1666667 0.7142857 3.061224 5
## [537] {machin} => {recent} 0.1666667 0.7142857 3.061224 5
## [538] {recent} => {learn} 0.1666667 0.7142857 1.648352 5
## [539] {recent} => {show} 0.2000000 0.8571429 1.607143 6
## [540] {recent} => {model} 0.2000000 0.8571429 1.607143 6
## [541] {machin} => {show} 0.1666667 0.7142857 1.339286 5
## [542] {machin} => {model} 0.1666667 0.7142857 1.339286 5
## [543] {machin} => {featur} 0.1666667 0.7142857 1.339286 5
## [544] {process} => {architectur} 0.1666667 0.8333333 3.125000 5
## [545] {process} => {algorithm} 0.1666667 0.8333333 2.083333 5
## [546] {process} => {featur} 0.1666667 0.8333333 1.562500 5
## [547] {process} => {network} 0.1666667 0.8333333 1.315789 5
## [548] {reduc} => {show} 0.1666667 0.7142857 1.339286 5
## [549] {reduc} => {network} 0.2000000 0.8571429 1.353383 6
## [550] {achiev} => {dataset} 0.1666667 0.7142857 1.648352 5
## [551] {achiev} => {propos} 0.1666667 0.7142857 1.428571 5
## [552] {achiev} => {featur} 0.1666667 0.7142857 1.339286 5
## [553] {achiev} => {network} 0.2000000 0.8571429 1.353383 6
## [554] {optim} => {problem} 0.1666667 0.7142857 2.380952 5
## [555] {optim} => {algorithm} 0.1666667 0.7142857 1.785714 5
## [556] {optim} => {perform} 0.1666667 0.7142857 1.530612 5
## [557] {signific} => {train} 0.2000000 0.7500000 1.875000 6
## [558] {success} => {represent} 0.2000000 0.7500000 1.500000 6
## [559] {experi} => {network} 0.2000000 0.7500000 1.184211 6
## [560] {object} => {show} 0.2000000 0.7500000 1.406250 6
## [561] {object} => {propos} 0.2333333 0.8750000 1.750000 7
## [562] {object} => {featur} 0.2000000 0.7500000 1.406250 6
## [563] {architectur} => {featur} 0.2000000 0.7500000 1.406250 6
## [564] {architectur} => {network} 0.2333333 0.8750000 1.381579 7
## [565] {make} => {perform} 0.2333333 0.7777778 1.666667 7
## [566] {make} => {model} 0.2333333 0.7777778 1.458333 7
## [567] {classif} => {method} 0.2000000 0.7500000 2.045455 6
## [568] {classif} => {show} 0.2000000 0.7500000 1.406250 6
## [569] {classif} => {propos} 0.2000000 0.7500000 1.500000 6
## [570] {classif} => {featur} 0.2333333 0.8750000 1.640625 7
## [571] {problem} => {perform} 0.2666667 0.8888889 1.904762 8
## [572] {improv} => {perform} 0.2333333 0.7777778 1.666667 7
## [573] {result} => {network} 0.2333333 0.7000000 1.105263 7
## [574] {neural} => {network} 0.3000000 0.9000000 1.421053 9
## [575] {method} => {show} 0.3000000 0.8181818 1.534091 9
## [576] {task} => {data} 0.3333333 0.9090909 2.097902 10
## [577] {data} => {task} 0.3333333 0.7692308 2.097902 10
## [578] {task} => {learn} 0.2666667 0.7272727 1.678322 8
## [579] {task} => {represent} 0.2666667 0.7272727 1.454545 8
## [580] {task} => {featur} 0.3000000 0.8181818 1.534091 9
## [581] {approach} => {propos} 0.3000000 0.7500000 1.500000 9
## [582] {work} => {network} 0.3333333 0.8333333 1.315789 10
## [583] {perform} => {propos} 0.3333333 0.7142857 1.428571 10
## [584] {data} => {featur} 0.3333333 0.7692308 1.442308 10
## [585] {dataset} => {propos} 0.3333333 0.7692308 1.538462 10
## [586] {learn} => {represent} 0.3333333 0.7692308 1.538462 10
## [587] {learn} => {featur} 0.3666667 0.8461538 1.586538 11
## [588] {represent} => {featur} 0.3666667 0.7333333 1.375000 11
## [589] {prior,
## represent} => {model} 0.1000000 1.0000000 1.875000 3
## [590] {model,
## prior} => {represent} 0.1000000 1.0000000 2.000000 3
## [591] {problem,
## shown} => {method} 0.1000000 1.0000000 2.727273 3
## [592] {method,
## shown} => {problem} 0.1000000 1.0000000 3.333333 3
## [593] {problem,
## shown} => {show} 0.1000000 1.0000000 1.875000 3
## [594] {show,
## shown} => {problem} 0.1000000 1.0000000 3.333333 3
## [595] {method,
## shown} => {show} 0.1000000 1.0000000 1.875000 3
## [596] {show,
## shown} => {method} 0.1000000 1.0000000 2.727273 3
## [597] {problem,
## convent} => {method} 0.1000000 1.0000000 2.727273 3
## [598] {method,
## convent} => {problem} 0.1000000 1.0000000 3.333333 3
## [599] {problem,
## convent} => {show} 0.1000000 1.0000000 1.875000 3
## [600] {show,
## convent} => {problem} 0.1000000 1.0000000 3.333333 3
## [601] {method,
## convent} => {show} 0.1000000 1.0000000 1.875000 3
## [602] {show,
## convent} => {method} 0.1000000 1.0000000 2.727273 3
## [603] {perform,
## current} => {dataset} 0.1000000 1.0000000 2.307692 3
## [604] {dataset,
## current} => {perform} 0.1000000 1.0000000 2.142857 3
## [605] {perform,
## current} => {featur} 0.1000000 1.0000000 1.875000 3
## [606] {featur,
## current} => {perform} 0.1000000 1.0000000 2.142857 3
## [607] {dataset,
## current} => {featur} 0.1000000 1.0000000 1.875000 3
## [608] {featur,
## current} => {dataset} 0.1000000 1.0000000 2.307692 3
## [609] {infer,
## make} => {paper} 0.1000000 1.0000000 3.000000 3
## [610] {infer,
## paper} => {make} 0.1000000 1.0000000 3.333333 3
## [611] {infer,
## make} => {data} 0.1000000 1.0000000 2.307692 3
## [612] {data,
## infer} => {make} 0.1000000 1.0000000 3.333333 3
## [613] {infer,
## make} => {model} 0.1000000 1.0000000 1.875000 3
## [614] {infer,
## model} => {make} 0.1000000 1.0000000 3.333333 3
## [615] {infer,
## paper} => {data} 0.1000000 1.0000000 2.307692 3
## [616] {data,
## infer} => {paper} 0.1000000 1.0000000 3.000000 3
## [617] {infer,
## paper} => {model} 0.1000000 1.0000000 1.875000 3
## [618] {infer,
## model} => {paper} 0.1000000 1.0000000 3.000000 3
## [619] {data,
## infer} => {model} 0.1000000 1.0000000 1.875000 3
## [620] {infer,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [621] {propos,
## human} => {featur} 0.1000000 1.0000000 1.875000 3
## [622] {featur,
## human} => {propos} 0.1000000 1.0000000 2.000000 3
## [623] {applic,
## bayesian} => {perform} 0.1000000 1.0000000 2.142857 3
## [624] {perform,
## bayesian} => {applic} 0.1000000 1.0000000 4.285714 3
## [625] {problem,
## address} => {perform} 0.1000000 1.0000000 2.142857 3
## [626] {perform,
## address} => {problem} 0.1000000 1.0000000 3.333333 3
## [627] {memori,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [628] {memori,
## network} => {reduc} 0.1000000 1.0000000 4.285714 3
## [629] {larger,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [630] {approach,
## larger} => {result} 0.1000000 1.0000000 3.000000 3
## [631] {larger,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [632] {dataset,
## larger} => {result} 0.1000000 1.0000000 3.000000 3
## [633] {larger,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [634] {larger,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [635] {larger,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [636] {model,
## larger} => {result} 0.1000000 1.0000000 3.000000 3
## [637] {approach,
## larger} => {dataset} 0.1000000 1.0000000 2.307692 3
## [638] {dataset,
## larger} => {approach} 0.1000000 1.0000000 2.500000 3
## [639] {approach,
## larger} => {propos} 0.1000000 1.0000000 2.000000 3
## [640] {larger,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [641] {approach,
## larger} => {model} 0.1000000 1.0000000 1.875000 3
## [642] {model,
## larger} => {approach} 0.1000000 1.0000000 2.500000 3
## [643] {dataset,
## larger} => {propos} 0.1000000 1.0000000 2.000000 3
## [644] {larger,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [645] {dataset,
## larger} => {model} 0.1000000 1.0000000 1.875000 3
## [646] {model,
## larger} => {dataset} 0.1000000 1.0000000 2.307692 3
## [647] {larger,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [648] {model,
## larger} => {propos} 0.1000000 1.0000000 2.000000 3
## [649] {label,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [650] {data,
## label} => {task} 0.1000000 1.0000000 2.727273 3
## [651] {label,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [652] {featur,
## label} => {task} 0.1000000 1.0000000 2.727273 3
## [653] {data,
## label} => {featur} 0.1000000 1.0000000 1.875000 3
## [654] {featur,
## label} => {data} 0.1000000 1.0000000 2.307692 3
## [655] {face,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [656] {challeng,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [657] {challeng,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [658] {face,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [659] {recognit,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [660] {recognit,
## face} => {ident} 0.1000000 0.7500000 7.500000 3
## [661] {face,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [662] {train,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [663] {train,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [664] {face,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [665] {represent,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [666] {represent,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [667] {challeng,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [668] {recognit,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [669] {recognit,
## challeng} => {ident} 0.1000000 1.0000000 10.000000 3
## [670] {challeng,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [671] {train,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [672] {train,
## challeng} => {ident} 0.1000000 0.7500000 7.500000 3
## [673] {challeng,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [674] {represent,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [675] {represent,
## challeng} => {ident} 0.1000000 0.7500000 7.500000 3
## [676] {recognit,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [677] {train,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [678] {train,
## recognit} => {ident} 0.1000000 0.7500000 7.500000 3
## [679] {recognit,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [680] {represent,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [681] {train,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [682] {represent,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [683] {make,
## standard} => {problem} 0.1000000 1.0000000 3.333333 3
## [684] {problem,
## standard} => {make} 0.1000000 1.0000000 3.333333 3
## [685] {make,
## standard} => {perform} 0.1000000 1.0000000 2.142857 3
## [686] {perform,
## standard} => {make} 0.1000000 1.0000000 3.333333 3
## [687] {make,
## standard} => {model} 0.1000000 1.0000000 1.875000 3
## [688] {model,
## standard} => {make} 0.1000000 1.0000000 3.333333 3
## [689] {problem,
## standard} => {perform} 0.1000000 1.0000000 2.142857 3
## [690] {perform,
## standard} => {problem} 0.1000000 1.0000000 3.333333 3
## [691] {problem,
## standard} => {model} 0.1000000 1.0000000 1.875000 3
## [692] {model,
## standard} => {problem} 0.1000000 1.0000000 3.333333 3
## [693] {perform,
## standard} => {model} 0.1000000 1.0000000 1.875000 3
## [694] {model,
## standard} => {perform} 0.1000000 1.0000000 2.142857 3
## [695] {dataset,
## pose} => {propos} 0.1000000 1.0000000 2.000000 3
## [696] {propos,
## pose} => {dataset} 0.1000000 1.0000000 2.307692 3
## [697] {dataset,
## pose} => {featur} 0.1000000 1.0000000 1.875000 3
## [698] {featur,
## pose} => {dataset} 0.1000000 1.0000000 2.307692 3
## [699] {propos,
## pose} => {featur} 0.1000000 1.0000000 1.875000 3
## [700] {featur,
## pose} => {propos} 0.1000000 1.0000000 2.000000 3
## [701] {shallow,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [702] {improv,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [703] {improv,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [704] {shallow,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [705] {perform,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [706] {perform,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [707] {shallow,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [708] {dataset,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [709] {dataset,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [710] {shallow,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [711] {featur,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [712] {featur,
## shallow} => {tradit} 0.1000000 0.7500000 7.500000 3
## [713] {improv,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [714] {perform,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [715] {improv,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [716] {dataset,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [717] {improv,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [718] {featur,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [719] {featur,
## improv} => {tradit} 0.1000000 0.7500000 7.500000 3
## [720] {perform,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [721] {dataset,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [722] {perform,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [723] {featur,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [724] {dataset,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [725] {featur,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [726] {perform,
## spars} => {represent} 0.1000000 1.0000000 2.000000 3
## [727] {represent,
## spars} => {perform} 0.1000000 1.0000000 2.142857 3
## [728] {perform,
## spars} => {propos} 0.1000000 1.0000000 2.000000 3
## [729] {propos,
## spars} => {perform} 0.1000000 1.0000000 2.142857 3
## [730] {represent,
## spars} => {propos} 0.1000000 1.0000000 2.000000 3
## [731] {propos,
## spars} => {represent} 0.1000000 1.0000000 2.000000 3
## [732] {task,
## oper} => {data} 0.1000000 1.0000000 2.307692 3
## [733] {data,
## oper} => {task} 0.1000000 1.0000000 2.727273 3
## [734] {perform,
## discrimin} => {learn} 0.1000000 1.0000000 2.307692 3
## [735] {learn,
## discrimin} => {perform} 0.1000000 1.0000000 2.142857 3
## [736] {perform,
## discrimin} => {model} 0.1000000 1.0000000 1.875000 3
## [737] {model,
## discrimin} => {perform} 0.1000000 1.0000000 2.142857 3
## [738] {perform,
## discrimin} => {featur} 0.1000000 1.0000000 1.875000 3
## [739] {featur,
## discrimin} => {perform} 0.1000000 1.0000000 2.142857 3
## [740] {learn,
## discrimin} => {model} 0.1000000 1.0000000 1.875000 3
## [741] {model,
## discrimin} => {learn} 0.1000000 1.0000000 2.307692 3
## [742] {learn,
## discrimin} => {featur} 0.1000000 1.0000000 1.875000 3
## [743] {featur,
## discrimin} => {learn} 0.1000000 1.0000000 2.307692 3
## [744] {model,
## discrimin} => {featur} 0.1000000 1.0000000 1.875000 3
## [745] {featur,
## discrimin} => {model} 0.1000000 1.0000000 1.875000 3
## [746] {show,
## construct} => {model} 0.1000000 1.0000000 1.875000 3
## [747] {model,
## construct} => {show} 0.1000000 1.0000000 1.875000 3
## [748] {advantag,
## classif} => {method} 0.1000000 1.0000000 2.727273 3
## [749] {advantag,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [750] {advantag,
## classif} => {approach} 0.1000000 1.0000000 2.500000 3
## [751] {advantag,
## approach} => {classif} 0.1000000 1.0000000 3.750000 3
## [752] {advantag,
## classif} => {featur} 0.1000000 1.0000000 1.875000 3
## [753] {advantag,
## featur} => {classif} 0.1000000 1.0000000 3.750000 3
## [754] {advantag,
## classif} => {network} 0.1000000 1.0000000 1.578947 3
## [755] {advantag,
## network} => {classif} 0.1000000 1.0000000 3.750000 3
## [756] {advantag,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [757] {advantag,
## approach} => {method} 0.1000000 1.0000000 2.727273 3
## [758] {advantag,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [759] {advantag,
## featur} => {method} 0.1000000 1.0000000 2.727273 3
## [760] {advantag,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [761] {advantag,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [762] {advantag,
## approach} => {featur} 0.1000000 1.0000000 1.875000 3
## [763] {advantag,
## featur} => {approach} 0.1000000 1.0000000 2.500000 3
## [764] {advantag,
## approach} => {network} 0.1000000 1.0000000 1.578947 3
## [765] {advantag,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [766] {advantag,
## featur} => {network} 0.1000000 1.0000000 1.578947 3
## [767] {advantag,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [768] {paper,
## perceptron} => {train} 0.1000000 1.0000000 2.500000 3
## [769] {train,
## perceptron} => {paper} 0.1000000 1.0000000 3.000000 3
## [770] {complex,
## equival} => {featur} 0.1000000 1.0000000 1.875000 3
## [771] {featur,
## equival} => {complex} 0.1000000 1.0000000 5.000000 3
## [772] {initi,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [773] {work,
## initi} => {layer} 0.1000000 1.0000000 5.000000 3
## [774] {work,
## layer} => {initi} 0.1000000 0.7500000 7.500000 3
## [775] {initi,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [776] {network,
## initi} => {layer} 0.1000000 1.0000000 5.000000 3
## [777] {network,
## layer} => {initi} 0.1000000 0.7500000 7.500000 3
## [778] {work,
## initi} => {network} 0.1000000 1.0000000 1.578947 3
## [779] {network,
## initi} => {work} 0.1000000 1.0000000 2.500000 3
## [780] {nonlinear,
## power} => {perform} 0.1000000 1.0000000 2.142857 3
## [781] {perform,
## nonlinear} => {power} 0.1000000 1.0000000 7.500000 3
## [782] {perform,
## power} => {nonlinear} 0.1000000 1.0000000 10.000000 3
## [783] {nonlinear,
## power} => {featur} 0.1000000 1.0000000 1.875000 3
## [784] {featur,
## nonlinear} => {power} 0.1000000 1.0000000 7.500000 3
## [785] {featur,
## power} => {nonlinear} 0.1000000 1.0000000 10.000000 3
## [786] {perform,
## nonlinear} => {featur} 0.1000000 1.0000000 1.875000 3
## [787] {featur,
## nonlinear} => {perform} 0.1000000 1.0000000 2.142857 3
## [788] {framework,
## finetun} => {train} 0.1000000 1.0000000 2.500000 3
## [789] {train,
## finetun} => {framework} 0.1000000 1.0000000 5.000000 3
## [790] {train,
## framework} => {finetun} 0.1000000 0.7500000 7.500000 3
## [791] {framework,
## finetun} => {work} 0.1000000 1.0000000 2.500000 3
## [792] {work,
## finetun} => {framework} 0.1000000 1.0000000 5.000000 3
## [793] {work,
## framework} => {finetun} 0.1000000 0.7500000 7.500000 3
## [794] {train,
## finetun} => {work} 0.1000000 1.0000000 2.500000 3
## [795] {work,
## finetun} => {train} 0.1000000 1.0000000 2.500000 3
## [796] {imag,
## local} => {perform} 0.1000000 1.0000000 2.142857 3
## [797] {perform,
## local} => {imag} 0.1000000 1.0000000 6.000000 3
## [798] {perform,
## imag} => {local} 0.1000000 0.7500000 7.500000 3
## [799] {imag,
## local} => {show} 0.1000000 1.0000000 1.875000 3
## [800] {show,
## local} => {imag} 0.1000000 1.0000000 6.000000 3
## [801] {show,
## imag} => {local} 0.1000000 1.0000000 10.000000 3
## [802] {imag,
## local} => {propos} 0.1000000 1.0000000 2.000000 3
## [803] {propos,
## local} => {imag} 0.1000000 1.0000000 6.000000 3
## [804] {perform,
## local} => {show} 0.1000000 1.0000000 1.875000 3
## [805] {show,
## local} => {perform} 0.1000000 1.0000000 2.142857 3
## [806] {perform,
## local} => {propos} 0.1000000 1.0000000 2.000000 3
## [807] {propos,
## local} => {perform} 0.1000000 1.0000000 2.142857 3
## [808] {show,
## local} => {propos} 0.1000000 1.0000000 2.000000 3
## [809] {propos,
## local} => {show} 0.1000000 1.0000000 1.875000 3
## [810] {layer,
## speech} => {result} 0.1000000 1.0000000 3.000000 3
## [811] {result,
## speech} => {layer} 0.1000000 1.0000000 5.000000 3
## [812] {machin,
## field} => {classif} 0.1000000 1.0000000 3.750000 3
## [813] {classif,
## field} => {machin} 0.1000000 1.0000000 4.285714 3
## [814] {classif,
## machin} => {field} 0.1000000 0.7500000 7.500000 3
## [815] {machin,
## field} => {learn} 0.1000000 1.0000000 2.307692 3
## [816] {learn,
## field} => {machin} 0.1000000 1.0000000 4.285714 3
## [817] {machin,
## learn} => {field} 0.1000000 0.7500000 7.500000 3
## [818] {machin,
## field} => {featur} 0.1000000 1.0000000 1.875000 3
## [819] {featur,
## field} => {machin} 0.1000000 1.0000000 4.285714 3
## [820] {classif,
## field} => {learn} 0.1000000 1.0000000 2.307692 3
## [821] {learn,
## field} => {classif} 0.1000000 1.0000000 3.750000 3
## [822] {classif,
## field} => {featur} 0.1000000 1.0000000 1.875000 3
## [823] {featur,
## field} => {classif} 0.1000000 1.0000000 3.750000 3
## [824] {learn,
## field} => {featur} 0.1000000 1.0000000 1.875000 3
## [825] {featur,
## field} => {learn} 0.1000000 1.0000000 2.307692 3
## [826] {result,
## engin} => {represent} 0.1000000 1.0000000 2.000000 3
## [827] {represent,
## engin} => {result} 0.1000000 1.0000000 3.000000 3
## [828] {result,
## engin} => {featur} 0.1000000 1.0000000 1.875000 3
## [829] {featur,
## engin} => {result} 0.1000000 1.0000000 3.000000 3
## [830] {represent,
## engin} => {featur} 0.1000000 1.0000000 1.875000 3
## [831] {featur,
## engin} => {represent} 0.1000000 1.0000000 2.000000 3
## [832] {demonstr,
## easili} => {task} 0.1000000 1.0000000 2.727273 3
## [833] {task,
## easili} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [834] {task,
## demonstr} => {easili} 0.1000000 1.0000000 10.000000 3
## [835] {demonstr,
## easili} => {data} 0.1000000 1.0000000 2.307692 3
## [836] {data,
## easili} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [837] {data,
## demonstr} => {easili} 0.1000000 1.0000000 10.000000 3
## [838] {task,
## easili} => {data} 0.1000000 1.0000000 2.307692 3
## [839] {data,
## easili} => {task} 0.1000000 1.0000000 2.727273 3
## [840] {input,
## form} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [841] {algorithm,
## form} => {input} 0.1000000 1.0000000 4.285714 3
## [842] {input,
## algorithm} => {form} 0.1000000 1.0000000 7.500000 3
## [843] {present,
## under} => {data} 0.1000000 1.0000000 2.307692 3
## [844] {data,
## under} => {present} 0.1000000 1.0000000 6.000000 3
## [845] {data,
## present} => {under} 0.1000000 1.0000000 7.500000 3
## [846] {present,
## under} => {show} 0.1000000 1.0000000 1.875000 3
## [847] {show,
## under} => {present} 0.1000000 0.7500000 4.500000 3
## [848] {present,
## show} => {under} 0.1000000 1.0000000 7.500000 3
## [849] {method,
## under} => {show} 0.1000000 1.0000000 1.875000 3
## [850] {show,
## under} => {method} 0.1000000 0.7500000 2.045455 3
## [851] {method,
## under} => {model} 0.1000000 1.0000000 1.875000 3
## [852] {model,
## under} => {method} 0.1000000 1.0000000 2.727273 3
## [853] {train,
## under} => {show} 0.1000000 1.0000000 1.875000 3
## [854] {show,
## under} => {train} 0.1000000 0.7500000 1.875000 3
## [855] {data,
## under} => {show} 0.1000000 1.0000000 1.875000 3
## [856] {show,
## under} => {data} 0.1000000 0.7500000 1.730769 3
## [857] {show,
## under} => {model} 0.1000000 0.7500000 1.406250 3
## [858] {model,
## under} => {show} 0.1000000 1.0000000 1.875000 3
## [859] {task,
## transfer} => {data} 0.1000000 1.0000000 2.307692 3
## [860] {data,
## transfer} => {task} 0.1000000 1.0000000 2.727273 3
## [861] {task,
## transfer} => {network} 0.1000000 1.0000000 1.578947 3
## [862] {network,
## transfer} => {task} 0.1000000 1.0000000 2.727273 3
## [863] {data,
## transfer} => {network} 0.1000000 1.0000000 1.578947 3
## [864] {network,
## transfer} => {data} 0.1000000 1.0000000 2.307692 3
## [865] {task,
## great} => {data} 0.1000000 1.0000000 2.307692 3
## [866] {data,
## great} => {task} 0.1000000 1.0000000 2.727273 3
## [867] {algorithm,
## condit} => {train} 0.1000000 1.0000000 2.500000 3
## [868] {train,
## condit} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [869] {algorithm,
## condit} => {propos} 0.1000000 1.0000000 2.000000 3
## [870] {propos,
## condit} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [871] {train,
## condit} => {propos} 0.1000000 1.0000000 2.000000 3
## [872] {propos,
## condit} => {train} 0.1000000 1.0000000 2.500000 3
## [873] {approach,
## common} => {dataset} 0.1000000 1.0000000 2.307692 3
## [874] {dataset,
## common} => {approach} 0.1000000 1.0000000 2.500000 3
## [875] {approach,
## common} => {show} 0.1000000 1.0000000 1.875000 3
## [876] {show,
## common} => {approach} 0.1000000 1.0000000 2.500000 3
## [877] {approach,
## common} => {propos} 0.1000000 1.0000000 2.000000 3
## [878] {propos,
## common} => {approach} 0.1000000 1.0000000 2.500000 3
## [879] {dataset,
## common} => {show} 0.1000000 1.0000000 1.875000 3
## [880] {show,
## common} => {dataset} 0.1000000 1.0000000 2.307692 3
## [881] {dataset,
## common} => {propos} 0.1000000 1.0000000 2.000000 3
## [882] {propos,
## common} => {dataset} 0.1000000 1.0000000 2.307692 3
## [883] {show,
## common} => {propos} 0.1000000 1.0000000 2.000000 3
## [884] {propos,
## common} => {show} 0.1000000 1.0000000 1.875000 3
## [885] {data,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [886] {dataset,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [887] {data,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [888] {learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [889] {data,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [890] {propos,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [891] {data,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [892] {model,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [893] {data,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [894] {featur,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [895] {dataset,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [896] {learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [897] {dataset,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [898] {propos,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [899] {dataset,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [900] {model,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [901] {dataset,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [902] {featur,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [903] {learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [904] {propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [905] {learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [906] {model,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [907] {learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [908] {featur,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [909] {propos,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [910] {model,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [911] {propos,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [912] {featur,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [913] {model,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [914] {featur,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [915] {compon,
## experi} => {dataset} 0.1000000 1.0000000 2.307692 3
## [916] {compon,
## dataset} => {experi} 0.1000000 1.0000000 3.750000 3
## [917] {dataset,
## experi} => {compon} 0.1000000 0.7500000 7.500000 3
## [918] {compon,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [919] {compon,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [920] {compon,
## experi} => {model} 0.1000000 1.0000000 1.875000 3
## [921] {model,
## compon} => {experi} 0.1000000 1.0000000 3.750000 3
## [922] {model,
## experi} => {compon} 0.1000000 0.7500000 7.500000 3
## [923] {compon,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [924] {compon,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [925] {compon,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [926] {model,
## compon} => {dataset} 0.1000000 1.0000000 2.307692 3
## [927] {compon,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [928] {model,
## compon} => {propos} 0.1000000 1.0000000 2.000000 3
## [929] {neural,
## provid} => {train} 0.1000000 1.0000000 2.500000 3
## [930] {train,
## provid} => {neural} 0.1000000 1.0000000 3.000000 3
## [931] {neural,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [932] {provid,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [933] {neural,
## provid} => {dataset} 0.1000000 1.0000000 2.307692 3
## [934] {dataset,
## provid} => {neural} 0.1000000 1.0000000 3.000000 3
## [935] {neural,
## provid} => {network} 0.1000000 1.0000000 1.578947 3
## [936] {network,
## provid} => {neural} 0.1000000 1.0000000 3.000000 3
## [937] {train,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [938] {provid,
## work} => {train} 0.1000000 1.0000000 2.500000 3
## [939] {train,
## provid} => {dataset} 0.1000000 1.0000000 2.307692 3
## [940] {dataset,
## provid} => {train} 0.1000000 1.0000000 2.500000 3
## [941] {train,
## provid} => {network} 0.1000000 1.0000000 1.578947 3
## [942] {network,
## provid} => {train} 0.1000000 1.0000000 2.500000 3
## [943] {provid,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [944] {dataset,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [945] {provid,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [946] {network,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [947] {dataset,
## provid} => {network} 0.1000000 1.0000000 1.578947 3
## [948] {network,
## provid} => {dataset} 0.1000000 1.0000000 2.307692 3
## [949] {task,
## multipl} => {data} 0.1000000 1.0000000 2.307692 3
## [950] {data,
## multipl} => {task} 0.1000000 1.0000000 2.727273 3
## [951] {object,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [952] {classif,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [953] {classif,
## object} => {test} 0.1000000 0.7500000 7.500000 3
## [954] {object,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [955] {propos,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [956] {object,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [957] {model,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [958] {object,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [959] {featur,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [960] {classif,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [961] {propos,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [962] {classif,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [963] {model,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [964] {classif,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [965] {featur,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [966] {propos,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [967] {model,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [968] {propos,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [969] {featur,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [970] {model,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [971] {featur,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [972] {extract,
## general} => {recognit} 0.1000000 1.0000000 3.333333 3
## [973] {extract,
## recognit} => {general} 0.1000000 1.0000000 5.000000 3
## [974] {general,
## recognit} => {extract} 0.1000000 0.7500000 5.625000 3
## [975] {extract,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [976] {extract,
## result} => {general} 0.1000000 0.7500000 3.750000 3
## [977] {general,
## result} => {extract} 0.1000000 0.7500000 5.625000 3
## [978] {extract,
## general} => {show} 0.1000000 1.0000000 1.875000 3
## [979] {show,
## extract} => {general} 0.1000000 0.7500000 3.750000 3
## [980] {extract,
## general} => {featur} 0.1000000 1.0000000 1.875000 3
## [981] {featur,
## extract} => {general} 0.1000000 1.0000000 5.000000 3
## [982] {featur,
## general} => {extract} 0.1000000 1.0000000 7.500000 3
## [983] {extract,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [984] {extract,
## result} => {recognit} 0.1000000 0.7500000 2.500000 3
## [985] {recognit,
## result} => {extract} 0.1000000 0.7500000 5.625000 3
## [986] {extract,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [987] {show,
## extract} => {recognit} 0.1000000 0.7500000 2.500000 3
## [988] {extract,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [989] {featur,
## extract} => {recognit} 0.1000000 1.0000000 3.333333 3
## [990] {extract,
## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [991] {algorithm,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [992] {extract,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [993] {data,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [994] {extract,
## result} => {show} 0.1333333 1.0000000 1.875000 4
## [995] {show,
## extract} => {result} 0.1333333 1.0000000 3.000000 4
## [996] {show,
## result} => {extract} 0.1333333 0.8000000 6.000000 4
## [997] {extract,
## result} => {model} 0.1000000 0.7500000 1.406250 3
## [998] {model,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [999] {extract,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [1000] {featur,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [1001] {algorithm,
## extract} => {show} 0.1000000 1.0000000 1.875000 3
## [1002] {show,
## extract} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1003] {algorithm,
## extract} => {model} 0.1000000 1.0000000 1.875000 3
## [1004] {model,
## extract} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1005] {data,
## extract} => {show} 0.1000000 1.0000000 1.875000 3
## [1006] {show,
## extract} => {data} 0.1000000 0.7500000 1.730769 3
## [1007] {show,
## extract} => {model} 0.1000000 0.7500000 1.406250 3
## [1008] {model,
## extract} => {show} 0.1000000 1.0000000 1.875000 3
## [1009] {show,
## extract} => {featur} 0.1000000 0.7500000 1.406250 3
## [1010] {featur,
## extract} => {show} 0.1000000 1.0000000 1.875000 3
## [1011] {appli,
## report} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1012] {dataset,
## report} => {appli} 0.1000000 1.0000000 5.000000 3
## [1013] {appli,
## dataset} => {report} 0.1000000 0.7500000 5.625000 3
## [1014] {appli,
## report} => {propos} 0.1000000 1.0000000 2.000000 3
## [1015] {propos,
## report} => {appli} 0.1000000 1.0000000 5.000000 3
## [1016] {appli,
## report} => {network} 0.1000000 1.0000000 1.578947 3
## [1017] {network,
## report} => {appli} 0.1000000 0.7500000 3.750000 3
## [1018] {network,
## appli} => {report} 0.1000000 0.7500000 5.625000 3
## [1019] {report,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [1020] {network,
## report} => {result} 0.1000000 0.7500000 2.250000 3
## [1021] {report,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [1022] {network,
## report} => {work} 0.1000000 0.7500000 1.875000 3
## [1023] {dataset,
## report} => {propos} 0.1000000 1.0000000 2.000000 3
## [1024] {propos,
## report} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1025] {dataset,
## report} => {network} 0.1000000 1.0000000 1.578947 3
## [1026] {network,
## report} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1027] {propos,
## report} => {network} 0.1000000 1.0000000 1.578947 3
## [1028] {network,
## report} => {propos} 0.1000000 0.7500000 1.500000 3
## [1029] {attribut,
## outperform} => {represent} 0.1000000 1.0000000 2.000000 3
## [1030] {attribut,
## represent} => {outperform} 0.1000000 1.0000000 7.500000 3
## [1031] {outperform,
## represent} => {attribut} 0.1000000 1.0000000 10.000000 3
## [1032] {attribut,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [1033] {attribut,
## show} => {outperform} 0.1000000 1.0000000 7.500000 3
## [1034] {outperform,
## show} => {attribut} 0.1000000 0.7500000 7.500000 3
## [1035] {attribut,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [1036] {attribut,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [1037] {employ,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [1038] {data,
## employ} => {task} 0.1000000 1.0000000 2.727273 3
## [1039] {employ,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [1040] {employ,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [1041] {employ,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [1042] {employ,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [1043] {data,
## employ} => {model} 0.1000000 1.0000000 1.875000 3
## [1044] {employ,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [1045] {data,
## employ} => {featur} 0.1000000 1.0000000 1.875000 3
## [1046] {employ,
## featur} => {data} 0.1000000 1.0000000 2.307692 3
## [1047] {employ,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [1048] {employ,
## featur} => {model} 0.1000000 1.0000000 1.875000 3
## [1049] {achiev,
## abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [1050] {approach,
## abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [1051] {approach,
## achiev} => {abil} 0.1000000 0.7500000 7.500000 3
## [1052] {achiev,
## abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [1053] {propos,
## abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [1054] {achiev,
## abil} => {network} 0.1000000 1.0000000 1.578947 3
## [1055] {network,
## abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [1056] {approach,
## abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [1057] {propos,
## abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [1058] {approach,
## abil} => {network} 0.1000000 1.0000000 1.578947 3
## [1059] {network,
## abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [1060] {propos,
## abil} => {network} 0.1000000 1.0000000 1.578947 3
## [1061] {network,
## abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [1062] {algorithm,
## structur} => {data} 0.1000000 1.0000000 2.307692 3
## [1063] {data,
## structur} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1064] {algorithm,
## structur} => {show} 0.1000000 1.0000000 1.875000 3
## [1065] {show,
## structur} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1066] {algorithm,
## structur} => {model} 0.1000000 1.0000000 1.875000 3
## [1067] {model,
## structur} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1068] {perform,
## structur} => {show} 0.1000000 1.0000000 1.875000 3
## [1069] {show,
## structur} => {perform} 0.1000000 0.7500000 1.607143 3
## [1070] {data,
## structur} => {show} 0.1000000 1.0000000 1.875000 3
## [1071] {show,
## structur} => {data} 0.1000000 0.7500000 1.730769 3
## [1072] {data,
## structur} => {model} 0.1000000 1.0000000 1.875000 3
## [1073] {model,
## structur} => {data} 0.1000000 1.0000000 2.307692 3
## [1074] {learn,
## structur} => {show} 0.1000000 1.0000000 1.875000 3
## [1075] {show,
## structur} => {learn} 0.1000000 0.7500000 1.730769 3
## [1076] {learn,
## structur} => {featur} 0.1000000 1.0000000 1.875000 3
## [1077] {featur,
## structur} => {learn} 0.1000000 1.0000000 2.307692 3
## [1078] {show,
## structur} => {model} 0.1000000 0.7500000 1.406250 3
## [1079] {model,
## structur} => {show} 0.1000000 1.0000000 1.875000 3
## [1080] {show,
## structur} => {featur} 0.1000000 0.7500000 1.406250 3
## [1081] {featur,
## structur} => {show} 0.1000000 1.0000000 1.875000 3
## [1082] {learn,
## inform} => {show} 0.1000000 1.0000000 1.875000 3
## [1083] {show,
## inform} => {learn} 0.1000000 1.0000000 2.307692 3
## [1084] {learn,
## inform} => {propos} 0.1000000 1.0000000 2.000000 3
## [1085] {propos,
## inform} => {learn} 0.1000000 1.0000000 2.307692 3
## [1086] {learn,
## inform} => {model} 0.1000000 1.0000000 1.875000 3
## [1087] {model,
## inform} => {learn} 0.1000000 1.0000000 2.307692 3
## [1088] {show,
## inform} => {propos} 0.1000000 1.0000000 2.000000 3
## [1089] {propos,
## inform} => {show} 0.1000000 1.0000000 1.875000 3
## [1090] {show,
## inform} => {model} 0.1000000 1.0000000 1.875000 3
## [1091] {model,
## inform} => {show} 0.1000000 1.0000000 1.875000 3
## [1092] {propos,
## inform} => {model} 0.1000000 1.0000000 1.875000 3
## [1093] {model,
## inform} => {propos} 0.1000000 1.0000000 2.000000 3
## [1094] {convolut,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [1095] {architectur,
## convolut} => {effici} 0.1000000 1.0000000 6.000000 3
## [1096] {architectur,
## effici} => {convolut} 0.1000000 0.7500000 5.625000 3
## [1097] {convolut,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [1098] {convolut,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [1099] {effici,
## work} => {convolut} 0.1000000 0.7500000 5.625000 3
## [1100] {convolut,
## effici} => {perform} 0.1000000 1.0000000 2.142857 3
## [1101] {perform,
## convolut} => {effici} 0.1000000 1.0000000 6.000000 3
## [1102] {perform,
## effici} => {convolut} 0.1000000 1.0000000 7.500000 3
## [1103] {convolut,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [1104] {network,
## convolut} => {effici} 0.1000000 0.7500000 4.500000 3
## [1105] {network,
## effici} => {convolut} 0.1000000 0.7500000 5.625000 3
## [1106] {reduc,
## convolut} => {network} 0.1000000 1.0000000 1.578947 3
## [1107] {network,
## convolut} => {reduc} 0.1000000 0.7500000 3.214286 3
## [1108] {architectur,
## convolut} => {work} 0.1000000 1.0000000 2.500000 3
## [1109] {convolut,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [1110] {architectur,
## convolut} => {perform} 0.1000000 1.0000000 2.142857 3
## [1111] {perform,
## convolut} => {architectur} 0.1000000 1.0000000 3.750000 3
## [1112] {architectur,
## convolut} => {network} 0.1000000 1.0000000 1.578947 3
## [1113] {network,
## convolut} => {architectur} 0.1000000 0.7500000 2.812500 3
## [1114] {convolut,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [1115] {perform,
## convolut} => {work} 0.1000000 1.0000000 2.500000 3
## [1116] {convolut,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [1117] {network,
## convolut} => {work} 0.1000000 0.7500000 1.875000 3
## [1118] {perform,
## convolut} => {network} 0.1000000 1.0000000 1.578947 3
## [1119] {network,
## convolut} => {perform} 0.1000000 0.7500000 1.607143 3
## [1120] {dataset,
## convolut} => {network} 0.1000000 1.0000000 1.578947 3
## [1121] {network,
## convolut} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1122] {larg,
## highlevel} => {work} 0.1000000 1.0000000 2.500000 3
## [1123] {work,
## highlevel} => {larg} 0.1000000 1.0000000 6.000000 3
## [1124] {larg,
## highlevel} => {represent} 0.1000000 1.0000000 2.000000 3
## [1125] {represent,
## highlevel} => {larg} 0.1000000 1.0000000 6.000000 3
## [1126] {represent,
## larg} => {highlevel} 0.1000000 0.7500000 7.500000 3
## [1127] {work,
## highlevel} => {represent} 0.1000000 1.0000000 2.000000 3
## [1128] {represent,
## highlevel} => {work} 0.1000000 1.0000000 2.500000 3
## [1129] {care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [1130] {task,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [1131] {task,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [1132] {care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [1133] {data,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [1134] {data,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [1135] {care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1136] {dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [1137] {dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [1138] {care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [1139] {learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [1140] {learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [1141] {care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [1142] {featur,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [1143] {featur,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [1144] {care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [1145] {network,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [1146] {network,
## design} => {care} 0.1000000 0.7500000 7.500000 3
## [1147] {task,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [1148] {data,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [1149] {task,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1150] {dataset,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [1151] {task,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [1152] {learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [1153] {task,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [1154] {featur,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [1155] {task,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [1156] {network,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [1157] {data,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1158] {dataset,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [1159] {data,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [1160] {learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [1161] {data,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [1162] {featur,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [1163] {data,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [1164] {network,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [1165] {dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [1166] {learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1167] {dataset,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [1168] {featur,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1169] {dataset,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [1170] {network,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1171] {learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [1172] {featur,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [1173] {learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [1174] {network,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [1175] {featur,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [1176] {network,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [1177] {dataset,
## rate} => {learn} 0.1000000 1.0000000 2.307692 3
## [1178] {learn,
## rate} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1179] {dataset,
## rate} => {represent} 0.1000000 1.0000000 2.000000 3
## [1180] {represent,
## rate} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1181] {dataset,
## rate} => {show} 0.1000000 1.0000000 1.875000 3
## [1182] {show,
## rate} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1183] {learn,
## rate} => {represent} 0.1000000 1.0000000 2.000000 3
## [1184] {represent,
## rate} => {learn} 0.1000000 1.0000000 2.307692 3
## [1185] {learn,
## rate} => {show} 0.1000000 1.0000000 1.875000 3
## [1186] {show,
## rate} => {learn} 0.1000000 1.0000000 2.307692 3
## [1187] {represent,
## rate} => {show} 0.1000000 1.0000000 1.875000 3
## [1188] {show,
## rate} => {represent} 0.1000000 1.0000000 2.000000 3
## [1189] {approach,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [1190] {captur,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [1191] {approach,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [1192] {represent,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [1193] {approach,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [1194] {featur,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [1195] {approach,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [1196] {network,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [1197] {captur,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [1198] {represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [1199] {captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [1200] {featur,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [1201] {captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [1202] {network,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [1203] {represent,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [1204] {featur,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [1205] {represent,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [1206] {network,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [1207] {featur,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [1208] {network,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [1209] {approach,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1210] {dataset,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [1211] {approach,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [1212] {propos,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [1213] {approach,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [1214] {network,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [1215] {dataset,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [1216] {propos,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1217] {dataset,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [1218] {network,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1219] {propos,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [1220] {network,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [1221] {reduc,
## time} => {model} 0.1000000 1.0000000 1.875000 3
## [1222] {model,
## time} => {reduc} 0.1000000 0.7500000 3.214286 3
## [1223] {model,
## reduc} => {time} 0.1000000 0.7500000 5.625000 3
## [1224] {show,
## time} => {model} 0.1000000 1.0000000 1.875000 3
## [1225] {model,
## time} => {show} 0.1000000 0.7500000 1.406250 3
## [1226] {model,
## time} => {network} 0.1000000 0.7500000 1.184211 3
## [1227] {network,
## time} => {model} 0.1000000 1.0000000 1.875000 3
## [1228] {simpl,
## studi} => {process} 0.1000000 1.0000000 5.000000 3
## [1229] {process,
## simpl} => {studi} 0.1000000 1.0000000 7.500000 3
## [1230] {process,
## studi} => {simpl} 0.1000000 1.0000000 10.000000 3
## [1231] {simpl,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [1232] {simpl,
## work} => {studi} 0.1000000 1.0000000 7.500000 3
## [1233] {studi,
## work} => {simpl} 0.1000000 1.0000000 10.000000 3
## [1234] {simpl,
## studi} => {network} 0.1000000 1.0000000 1.578947 3
## [1235] {network,
## simpl} => {studi} 0.1000000 1.0000000 7.500000 3
## [1236] {network,
## studi} => {simpl} 0.1000000 1.0000000 10.000000 3
## [1237] {process,
## simpl} => {work} 0.1000000 1.0000000 2.500000 3
## [1238] {simpl,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [1239] {process,
## work} => {simpl} 0.1000000 0.7500000 7.500000 3
## [1240] {process,
## simpl} => {network} 0.1000000 1.0000000 1.578947 3
## [1241] {network,
## simpl} => {process} 0.1000000 1.0000000 5.000000 3
## [1242] {simpl,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [1243] {network,
## simpl} => {work} 0.1000000 1.0000000 2.500000 3
## [1244] {classif,
## baselin} => {propos} 0.1000000 1.0000000 2.000000 3
## [1245] {propos,
## baselin} => {classif} 0.1000000 1.0000000 3.750000 3
## [1246] {result,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [1247] {train,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [1248] {result,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1249] {dataset,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [1250] {result,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1251] {network,
## increas} => {result} 0.1000000 0.7500000 2.250000 3
## [1252] {neural,
## increas} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1253] {algorithm,
## increas} => {neural} 0.1000000 1.0000000 3.000000 3
## [1254] {neural,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1255] {network,
## increas} => {neural} 0.1000000 0.7500000 2.250000 3
## [1256] {algorithm,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1257] {network,
## increas} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1258] {train,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1259] {dataset,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [1260] {train,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1261] {network,
## increas} => {train} 0.1000000 0.7500000 1.875000 3
## [1262] {dataset,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1263] {network,
## increas} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1264] {represent,
## increas} => {featur} 0.1000000 1.0000000 1.875000 3
## [1265] {featur,
## increas} => {represent} 0.1000000 1.0000000 2.000000 3
## [1266] {represent,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1267] {network,
## increas} => {represent} 0.1000000 0.7500000 1.500000 3
## [1268] {featur,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1269] {network,
## increas} => {featur} 0.1000000 0.7500000 1.406250 3
## [1270] {boltzmann,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [1271] {boltzmann,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1272] {restrict,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1273] {boltzmann,
## restrict} => {machin} 0.1333333 1.0000000 4.285714 4
## [1274] {boltzmann,
## machin} => {restrict} 0.1333333 1.0000000 7.500000 4
## [1275] {machin,
## restrict} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [1276] {boltzmann,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1277] {boltzmann,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1278] {restrict,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1279] {boltzmann,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [1280] {boltzmann,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1281] {restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1282] {boltzmann,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [1283] {boltzmann,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1284] {restrict,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1285] {boltzmann,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [1286] {boltzmann,
## data} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1287] {data,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1288] {boltzmann,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [1289] {boltzmann,
## show} => {restrict} 0.1333333 1.0000000 7.500000 4
## [1290] {restrict,
## show} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [1291] {boltzmann,
## restrict} => {model} 0.1333333 1.0000000 1.875000 4
## [1292] {boltzmann,
## model} => {restrict} 0.1333333 1.0000000 7.500000 4
## [1293] {model,
## restrict} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [1294] {boltzmann,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [1295] {boltzmann,
## featur} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1296] {featur,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1297] {boltzmann,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [1298] {boltzmann,
## machin} => {recent} 0.1000000 0.7500000 3.214286 3
## [1299] {boltzmann,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1300] {boltzmann,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [1301] {algorithm,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [1302] {boltzmann,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [1303] {boltzmann,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [1304] {boltzmann,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [1305] {boltzmann,
## model} => {recent} 0.1000000 0.7500000 3.214286 3
## [1306] {boltzmann,
## machin} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1307] {boltzmann,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [1308] {machin,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1309] {boltzmann,
## machin} => {task} 0.1000000 0.7500000 2.045455 3
## [1310] {boltzmann,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [1311] {machin,
## task} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [1312] {boltzmann,
## machin} => {train} 0.1000000 0.7500000 1.875000 3
## [1313] {boltzmann,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [1314] {machin,
## train} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [1315] {boltzmann,
## machin} => {data} 0.1000000 0.7500000 1.730769 3
## [1316] {boltzmann,
## data} => {machin} 0.1000000 1.0000000 4.285714 3
## [1317] {data,
## machin} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1318] {boltzmann,
## machin} => {show} 0.1333333 1.0000000 1.875000 4
## [1319] {boltzmann,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [1320] {machin,
## show} => {boltzmann} 0.1333333 0.8000000 6.000000 4
## [1321] {boltzmann,
## machin} => {model} 0.1333333 1.0000000 1.875000 4
## [1322] {boltzmann,
## model} => {machin} 0.1333333 1.0000000 4.285714 4
## [1323] {machin,
## model} => {boltzmann} 0.1333333 0.8000000 6.000000 4
## [1324] {boltzmann,
## machin} => {featur} 0.1000000 0.7500000 1.406250 3
## [1325] {boltzmann,
## featur} => {machin} 0.1000000 1.0000000 4.285714 3
## [1326] {boltzmann,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [1327] {boltzmann,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1328] {boltzmann,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [1329] {boltzmann,
## model} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1330] {boltzmann,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [1331] {boltzmann,
## data} => {task} 0.1000000 1.0000000 2.727273 3
## [1332] {boltzmann,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [1333] {boltzmann,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [1334] {boltzmann,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [1335] {boltzmann,
## model} => {task} 0.1000000 0.7500000 2.045455 3
## [1336] {boltzmann,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [1337] {boltzmann,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [1338] {boltzmann,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [1339] {boltzmann,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [1340] {boltzmann,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [1341] {boltzmann,
## model} => {train} 0.1000000 0.7500000 1.875000 3
## [1342] {boltzmann,
## data} => {show} 0.1000000 1.0000000 1.875000 3
## [1343] {boltzmann,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [1344] {boltzmann,
## data} => {model} 0.1000000 1.0000000 1.875000 3
## [1345] {boltzmann,
## model} => {data} 0.1000000 0.7500000 1.730769 3
## [1346] {boltzmann,
## data} => {featur} 0.1000000 1.0000000 1.875000 3
## [1347] {boltzmann,
## featur} => {data} 0.1000000 1.0000000 2.307692 3
## [1348] {boltzmann,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [1349] {boltzmann,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [1350] {boltzmann,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [1351] {boltzmann,
## featur} => {show} 0.1000000 1.0000000 1.875000 3
## [1352] {boltzmann,
## model} => {featur} 0.1000000 0.7500000 1.406250 3
## [1353] {boltzmann,
## featur} => {model} 0.1000000 1.0000000 1.875000 3
## [1354] {creat,
## model} => {network} 0.1000000 1.0000000 1.578947 3
## [1355] {creat,
## network} => {model} 0.1000000 0.7500000 1.406250 3
## [1356] {restrict,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [1357] {machin,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [1358] {restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1359] {restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [1360] {algorithm,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [1361] {restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [1362] {restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [1363] {restrict,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [1364] {model,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [1365] {machin,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1366] {restrict,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [1367] {machin,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1368] {machin,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [1369] {restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [1370] {machin,
## task} => {restrict} 0.1000000 0.7500000 5.625000 3
## [1371] {machin,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [1372] {restrict,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [1373] {machin,
## train} => {restrict} 0.1000000 0.7500000 5.625000 3
## [1374] {machin,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [1375] {data,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [1376] {data,
## machin} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1377] {machin,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [1378] {restrict,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [1379] {machin,
## show} => {restrict} 0.1333333 0.8000000 6.000000 4
## [1380] {machin,
## restrict} => {model} 0.1333333 1.0000000 1.875000 4
## [1381] {model,
## restrict} => {machin} 0.1333333 1.0000000 4.285714 4
## [1382] {machin,
## model} => {restrict} 0.1333333 0.8000000 6.000000 4
## [1383] {machin,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [1384] {featur,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [1385] {restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [1386] {restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1387] {restrict,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [1388] {model,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1389] {restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [1390] {data,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [1391] {restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [1392] {restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [1393] {restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [1394] {model,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [1395] {restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [1396] {featur,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [1397] {restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [1398] {restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [1399] {restrict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [1400] {model,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [1401] {data,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [1402] {restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [1403] {data,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [1404] {model,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [1405] {data,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [1406] {featur,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [1407] {restrict,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [1408] {model,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [1409] {restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [1410] {featur,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [1411] {model,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [1412] {featur,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [1413] {dataset,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [1414] {learn,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1415] {dataset,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [1416] {propos,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1417] {dataset,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [1418] {model,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1419] {learn,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [1420] {propos,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [1421] {learn,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [1422] {model,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [1423] {propos,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [1424] {model,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [1425] {make,
## present} => {model} 0.1000000 1.0000000 1.875000 3
## [1426] {model,
## present} => {make} 0.1000000 1.0000000 3.333333 3
## [1427] {data,
## present} => {show} 0.1000000 1.0000000 1.875000 3
## [1428] {present,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [1429] {neural,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1430] {algorithm,
## order} => {neural} 0.1000000 0.7500000 2.250000 3
## [1431] {neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [1432] {approach,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [1433] {neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [1434] {model,
## order} => {neural} 0.1000000 0.7500000 2.250000 3
## [1435] {neural,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [1436] {network,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [1437] {algorithm,
## order} => {approach} 0.1000000 0.7500000 1.875000 3
## [1438] {approach,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1439] {algorithm,
## order} => {show} 0.1000000 0.7500000 1.406250 3
## [1440] {show,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1441] {algorithm,
## order} => {propos} 0.1000000 0.7500000 1.500000 3
## [1442] {order,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1443] {algorithm,
## order} => {model} 0.1333333 1.0000000 1.875000 4
## [1444] {model,
## order} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [1445] {algorithm,
## order} => {featur} 0.1000000 0.7500000 1.406250 3
## [1446] {featur,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1447] {algorithm,
## order} => {network} 0.1000000 0.7500000 1.184211 3
## [1448] {network,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1449] {approach,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [1450] {model,
## order} => {approach} 0.1000000 0.7500000 1.875000 3
## [1451] {approach,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [1452] {network,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [1453] {show,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [1454] {model,
## order} => {show} 0.1000000 0.7500000 1.406250 3
## [1455] {show,
## order} => {featur} 0.1000000 1.0000000 1.875000 3
## [1456] {featur,
## order} => {show} 0.1000000 1.0000000 1.875000 3
## [1457] {order,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [1458] {model,
## order} => {propos} 0.1000000 0.7500000 1.500000 3
## [1459] {model,
## order} => {featur} 0.1000000 0.7500000 1.406250 3
## [1460] {featur,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [1461] {model,
## order} => {network} 0.1000000 0.7500000 1.184211 3
## [1462] {network,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [1463] {power,
## specif} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1464] {algorithm,
## power} => {specif} 0.1000000 1.0000000 6.000000 3
## [1465] {algorithm,
## specif} => {power} 0.1000000 0.7500000 5.625000 3
## [1466] {power,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [1467] {train,
## power} => {specif} 0.1000000 1.0000000 6.000000 3
## [1468] {improv,
## power} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1469] {dataset,
## power} => {improv} 0.1000000 1.0000000 3.333333 3
## [1470] {algorithm,
## power} => {train} 0.1000000 1.0000000 2.500000 3
## [1471] {train,
## power} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1472] {perform,
## power} => {featur} 0.1000000 1.0000000 1.875000 3
## [1473] {featur,
## power} => {perform} 0.1000000 1.0000000 2.142857 3
## [1474] {demonstr,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1475] {dataset,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [1476] {dataset,
## demonstr} => {benchmark} 0.1000000 0.7500000 5.625000 3
## [1477] {demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1478] {learn,
## benchmark} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [1479] {demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [1480] {demonstr,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [1481] {model,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [1482] {model,
## demonstr} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [1483] {demonstr,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1484] {featur,
## benchmark} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [1485] {featur,
## demonstr} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [1486] {classif,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1487] {learn,
## benchmark} => {classif} 0.1000000 0.7500000 2.812500 3
## [1488] {classif,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1489] {featur,
## benchmark} => {classif} 0.1000000 0.7500000 2.812500 3
## [1490] {problem,
## benchmark} => {perform} 0.1000000 1.0000000 2.142857 3
## [1491] {perform,
## benchmark} => {problem} 0.1000000 1.0000000 3.333333 3
## [1492] {problem,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1493] {learn,
## benchmark} => {problem} 0.1000000 0.7500000 2.500000 3
## [1494] {problem,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1495] {featur,
## benchmark} => {problem} 0.1000000 0.7500000 2.500000 3
## [1496] {perform,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1497] {learn,
## benchmark} => {perform} 0.1000000 0.7500000 1.607143 3
## [1498] {perform,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1499] {featur,
## benchmark} => {perform} 0.1000000 0.7500000 1.607143 3
## [1500] {dataset,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1501] {learn,
## benchmark} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1502] {dataset,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [1503] {model,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1504] {dataset,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1505] {featur,
## benchmark} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1506] {learn,
## benchmark} => {propos} 0.1000000 0.7500000 1.500000 3
## [1507] {propos,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1508] {learn,
## benchmark} => {model} 0.1000000 0.7500000 1.406250 3
## [1509] {model,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1510] {learn,
## benchmark} => {featur} 0.1333333 1.0000000 1.875000 4
## [1511] {featur,
## benchmark} => {learn} 0.1333333 1.0000000 2.307692 4
## [1512] {propos,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1513] {featur,
## benchmark} => {propos} 0.1000000 0.7500000 1.500000 3
## [1514] {model,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1515] {featur,
## benchmark} => {model} 0.1000000 0.7500000 1.406250 3
## [1516] {success,
## high} => {represent} 0.1000000 1.0000000 2.000000 3
## [1517] {represent,
## high} => {success} 0.1000000 1.0000000 3.750000 3
## [1518] {success,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1519] {propos,
## high} => {success} 0.1000000 0.7500000 2.812500 3
## [1520] {success,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1521] {featur,
## high} => {success} 0.1000000 0.7500000 2.812500 3
## [1522] {object,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1523] {propos,
## high} => {object} 0.1000000 0.7500000 2.812500 3
## [1524] {object,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1525] {featur,
## high} => {object} 0.1000000 0.7500000 2.812500 3
## [1526] {classif,
## high} => {learn} 0.1000000 1.0000000 2.307692 3
## [1527] {learn,
## high} => {classif} 0.1000000 1.0000000 3.750000 3
## [1528] {classif,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1529] {propos,
## high} => {classif} 0.1000000 0.7500000 2.812500 3
## [1530] {classif,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1531] {featur,
## high} => {classif} 0.1000000 0.7500000 2.812500 3
## [1532] {task,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1533] {propos,
## high} => {task} 0.1000000 0.7500000 2.045455 3
## [1534] {task,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1535] {featur,
## high} => {task} 0.1000000 0.7500000 2.045455 3
## [1536] {learn,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1537] {propos,
## high} => {learn} 0.1000000 0.7500000 1.730769 3
## [1538] {learn,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1539] {featur,
## high} => {learn} 0.1000000 0.7500000 1.730769 3
## [1540] {represent,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1541] {propos,
## high} => {represent} 0.1000000 0.7500000 1.500000 3
## [1542] {represent,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1543] {featur,
## high} => {represent} 0.1000000 0.7500000 1.500000 3
## [1544] {propos,
## high} => {featur} 0.1333333 1.0000000 1.875000 4
## [1545] {featur,
## high} => {propos} 0.1333333 1.0000000 2.000000 4
## [1546] {parallel,
## framework} => {network} 0.1000000 1.0000000 1.578947 3
## [1547] {network,
## parallel} => {framework} 0.1000000 0.7500000 3.750000 3
## [1548] {network,
## framework} => {parallel} 0.1000000 0.7500000 5.625000 3
## [1549] {comput,
## parallel} => {reduc} 0.1000000 1.0000000 4.285714 3
## [1550] {reduc,
## parallel} => {comput} 0.1000000 1.0000000 4.285714 3
## [1551] {reduc,
## comput} => {parallel} 0.1000000 0.7500000 5.625000 3
## [1552] {comput,
## parallel} => {network} 0.1000000 1.0000000 1.578947 3
## [1553] {network,
## parallel} => {comput} 0.1000000 0.7500000 3.214286 3
## [1554] {reduc,
## parallel} => {network} 0.1000000 1.0000000 1.578947 3
## [1555] {network,
## parallel} => {reduc} 0.1000000 0.7500000 3.214286 3
## [1556] {parallel,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [1557] {network,
## parallel} => {work} 0.1000000 0.7500000 1.875000 3
## [1558] {outperform,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [1559] {predict,
## train} => {outperform} 0.1000000 0.7500000 5.625000 3
## [1560] {outperform,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [1561] {outperform,
## predict} => {show} 0.1000000 1.0000000 1.875000 3
## [1562] {predict,
## show} => {outperform} 0.1000000 1.0000000 7.500000 3
## [1563] {outperform,
## show} => {predict} 0.1000000 0.7500000 5.625000 3
## [1564] {predict,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [1565] {predict,
## train} => {challeng} 0.1000000 0.7500000 4.500000 3
## [1566] {train,
## challeng} => {predict} 0.1000000 0.7500000 5.625000 3
## [1567] {predict,
## challeng} => {perform} 0.1000000 1.0000000 2.142857 3
## [1568] {predict,
## perform} => {challeng} 0.1000000 1.0000000 6.000000 3
## [1569] {perform,
## challeng} => {predict} 0.1000000 1.0000000 7.500000 3
## [1570] {predict,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [1571] {predict,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [1572] {propos,
## challeng} => {predict} 0.1000000 0.7500000 5.625000 3
## [1573] {paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [1574] {predict,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [1575] {paper,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [1576] {data,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [1577] {paper,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [1578] {model,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [1579] {paper,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [1580] {featur,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [1581] {predict,
## train} => {perform} 0.1000000 0.7500000 1.607143 3
## [1582] {predict,
## perform} => {train} 0.1000000 1.0000000 2.500000 3
## [1583] {predict,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [1584] {data,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [1585] {predict,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [1586] {predict,
## represent} => {train} 0.1000000 1.0000000 2.500000 3
## [1587] {predict,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [1588] {predict,
## show} => {train} 0.1000000 1.0000000 2.500000 3
## [1589] {predict,
## train} => {propos} 0.1000000 0.7500000 1.500000 3
## [1590] {predict,
## propos} => {train} 0.1000000 1.0000000 2.500000 3
## [1591] {predict,
## train} => {model} 0.1000000 0.7500000 1.406250 3
## [1592] {model,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [1593] {predict,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [1594] {featur,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [1595] {predict,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [1596] {predict,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [1597] {data,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [1598] {model,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [1599] {data,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [1600] {featur,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [1601] {model,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [1602] {featur,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [1603] {number,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [1604] {number,
## perform} => {applic} 0.1000000 1.0000000 4.285714 3
## [1605] {make,
## number} => {data} 0.1000000 1.0000000 2.307692 3
## [1606] {data,
## number} => {make} 0.1000000 1.0000000 3.333333 3
## [1607] {make,
## number} => {model} 0.1000000 1.0000000 1.875000 3
## [1608] {model,
## number} => {make} 0.1000000 0.7500000 2.500000 3
## [1609] {number,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [1610] {model,
## number} => {result} 0.1000000 0.7500000 2.250000 3
## [1611] {method,
## number} => {show} 0.1000000 1.0000000 1.875000 3
## [1612] {number,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [1613] {approach,
## number} => {model} 0.1000000 1.0000000 1.875000 3
## [1614] {model,
## number} => {approach} 0.1000000 0.7500000 1.875000 3
## [1615] {data,
## number} => {model} 0.1000000 1.0000000 1.875000 3
## [1616] {model,
## number} => {data} 0.1000000 0.7500000 1.730769 3
## [1617] {number,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [1618] {featur,
## number} => {represent} 0.1000000 1.0000000 2.000000 3
## [1619] {face,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1620] {recognit,
## face} => {variat} 0.1000000 0.7500000 5.625000 3
## [1621] {recognit,
## variat} => {face} 0.1000000 0.7500000 5.625000 3
## [1622] {challeng,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1623] {recognit,
## face} => {challeng} 0.1000000 0.7500000 4.500000 3
## [1624] {recognit,
## challeng} => {face} 0.1000000 1.0000000 7.500000 3
## [1625] {challeng,
## face} => {train} 0.1000000 1.0000000 2.500000 3
## [1626] {train,
## face} => {challeng} 0.1000000 1.0000000 6.000000 3
## [1627] {train,
## challeng} => {face} 0.1000000 0.7500000 5.625000 3
## [1628] {challeng,
## face} => {represent} 0.1000000 1.0000000 2.000000 3
## [1629] {represent,
## face} => {challeng} 0.1000000 1.0000000 6.000000 3
## [1630] {represent,
## challeng} => {face} 0.1000000 0.7500000 5.625000 3
## [1631] {imag,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1632] {recognit,
## face} => {imag} 0.1000000 0.7500000 4.500000 3
## [1633] {recognit,
## imag} => {face} 0.1000000 0.7500000 5.625000 3
## [1634] {imag,
## face} => {propos} 0.1000000 1.0000000 2.000000 3
## [1635] {propos,
## face} => {imag} 0.1000000 1.0000000 6.000000 3
## [1636] {recognit,
## face} => {train} 0.1000000 0.7500000 1.875000 3
## [1637] {train,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1638] {train,
## recognit} => {face} 0.1000000 0.7500000 5.625000 3
## [1639] {recognit,
## face} => {data} 0.1000000 0.7500000 1.730769 3
## [1640] {data,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1641] {recognit,
## face} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1642] {dataset,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1643] {recognit,
## face} => {learn} 0.1000000 0.7500000 1.730769 3
## [1644] {learn,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1645] {recognit,
## face} => {represent} 0.1000000 0.7500000 1.500000 3
## [1646] {represent,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1647] {recognit,
## face} => {propos} 0.1000000 0.7500000 1.500000 3
## [1648] {propos,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1649] {recognit,
## face} => {featur} 0.1000000 0.7500000 1.406250 3
## [1650] {featur,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1651] {train,
## face} => {represent} 0.1000000 1.0000000 2.000000 3
## [1652] {represent,
## face} => {train} 0.1000000 1.0000000 2.500000 3
## [1653] {data,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1654] {dataset,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [1655] {data,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [1656] {learn,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [1657] {data,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [1658] {featur,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [1659] {dataset,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [1660] {learn,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1661] {dataset,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [1662] {featur,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1663] {learn,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [1664] {featur,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [1665] {method,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [1666] {approach,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [1667] {method,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1668] {dataset,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [1669] {method,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [1670] {learn,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [1671] {method,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [1672] {show,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [1673] {method,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [1674] {model,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [1675] {approach,
## sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [1676] {dataset,
## sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [1677] {approach,
## sourc} => {learn} 0.1333333 1.0000000 2.307692 4
## [1678] {learn,
## sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [1679] {approach,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [1680] {represent,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [1681] {approach,
## sourc} => {show} 0.1333333 1.0000000 1.875000 4
## [1682] {show,
## sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [1683] {approach,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [1684] {propos,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [1685] {approach,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [1686] {model,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [1687] {dataset,
## sourc} => {learn} 0.1333333 1.0000000 2.307692 4
## [1688] {learn,
## sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [1689] {dataset,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [1690] {represent,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1691] {dataset,
## sourc} => {show} 0.1333333 1.0000000 1.875000 4
## [1692] {show,
## sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [1693] {dataset,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [1694] {propos,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1695] {dataset,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [1696] {model,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1697] {learn,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [1698] {represent,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [1699] {learn,
## sourc} => {show} 0.1333333 1.0000000 1.875000 4
## [1700] {show,
## sourc} => {learn} 0.1333333 1.0000000 2.307692 4
## [1701] {learn,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [1702] {propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [1703] {learn,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [1704] {model,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [1705] {represent,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [1706] {show,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [1707] {represent,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [1708] {propos,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [1709] {show,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [1710] {propos,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [1711] {show,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [1712] {model,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [1713] {general,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1714] {recognit,
## variat} => {general} 0.1000000 0.7500000 3.750000 3
## [1715] {general,
## recognit} => {variat} 0.1000000 0.7500000 5.625000 3
## [1716] {general,
## variat} => {represent} 0.1000000 1.0000000 2.000000 3
## [1717] {represent,
## variat} => {general} 0.1000000 1.0000000 5.000000 3
## [1718] {represent,
## general} => {variat} 0.1000000 0.7500000 5.625000 3
## [1719] {general,
## variat} => {show} 0.1000000 1.0000000 1.875000 3
## [1720] {show,
## variat} => {general} 0.1000000 1.0000000 5.000000 3
## [1721] {recognit,
## variat} => {task} 0.1000000 0.7500000 2.045455 3
## [1722] {task,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1723] {task,
## recognit} => {variat} 0.1000000 0.7500000 5.625000 3
## [1724] {recognit,
## variat} => {data} 0.1000000 0.7500000 1.730769 3
## [1725] {data,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1726] {recognit,
## variat} => {learn} 0.1000000 0.7500000 1.730769 3
## [1727] {learn,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1728] {recognit,
## variat} => {represent} 0.1000000 0.7500000 1.500000 3
## [1729] {represent,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1730] {recognit,
## variat} => {show} 0.1000000 0.7500000 1.406250 3
## [1731] {show,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1732] {recognit,
## variat} => {featur} 0.1000000 0.7500000 1.406250 3
## [1733] {featur,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1734] {task,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [1735] {data,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [1736] {task,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [1737] {learn,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [1738] {task,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [1739] {featur,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [1740] {data,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [1741] {learn,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [1742] {data,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [1743] {featur,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [1744] {learn,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [1745] {featur,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [1746] {represent,
## variat} => {show} 0.1000000 1.0000000 1.875000 3
## [1747] {show,
## variat} => {represent} 0.1000000 1.0000000 2.000000 3
## [1748] {process,
## introduc} => {learn} 0.1000000 1.0000000 2.307692 3
## [1749] {introduc,
## learn} => {process} 0.1000000 1.0000000 5.000000 3
## [1750] {process,
## learn} => {introduc} 0.1000000 1.0000000 7.500000 3
## [1751] {process,
## introduc} => {model} 0.1000000 1.0000000 1.875000 3
## [1752] {model,
## introduc} => {process} 0.1000000 1.0000000 5.000000 3
## [1753] {model,
## process} => {introduc} 0.1000000 0.7500000 5.625000 3
## [1754] {process,
## introduc} => {featur} 0.1000000 1.0000000 1.875000 3
## [1755] {featur,
## introduc} => {process} 0.1000000 1.0000000 5.000000 3
## [1756] {task,
## introduc} => {data} 0.1000000 1.0000000 2.307692 3
## [1757] {data,
## introduc} => {task} 0.1000000 1.0000000 2.727273 3
## [1758] {introduc,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [1759] {model,
## introduc} => {learn} 0.1000000 1.0000000 2.307692 3
## [1760] {introduc,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [1761] {featur,
## introduc} => {learn} 0.1000000 1.0000000 2.307692 3
## [1762] {model,
## introduc} => {featur} 0.1000000 1.0000000 1.875000 3
## [1763] {featur,
## introduc} => {model} 0.1000000 1.0000000 1.875000 3
## [1764] {import,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [1765] {approach,
## import} => {task} 0.1000000 1.0000000 2.727273 3
## [1766] {import,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [1767] {data,
## import} => {task} 0.1333333 1.0000000 2.727273 4
## [1768] {import,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [1769] {import,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [1770] {import,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [1771] {import,
## represent} => {task} 0.1333333 1.0000000 2.727273 4
## [1772] {import,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [1773] {import,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [1774] {import,
## task} => {model} 0.1333333 1.0000000 1.875000 4
## [1775] {import,
## model} => {task} 0.1333333 1.0000000 2.727273 4
## [1776] {import,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [1777] {featur,
## import} => {task} 0.1333333 1.0000000 2.727273 4
## [1778] {approach,
## import} => {data} 0.1000000 1.0000000 2.307692 3
## [1779] {data,
## import} => {approach} 0.1000000 0.7500000 1.875000 3
## [1780] {approach,
## import} => {represent} 0.1000000 1.0000000 2.000000 3
## [1781] {import,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [1782] {approach,
## import} => {model} 0.1000000 1.0000000 1.875000 3
## [1783] {import,
## model} => {approach} 0.1000000 0.7500000 1.875000 3
## [1784] {approach,
## import} => {featur} 0.1000000 1.0000000 1.875000 3
## [1785] {featur,
## import} => {approach} 0.1000000 0.7500000 1.875000 3
## [1786] {data,
## import} => {learn} 0.1000000 0.7500000 1.730769 3
## [1787] {import,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [1788] {data,
## import} => {represent} 0.1333333 1.0000000 2.000000 4
## [1789] {import,
## represent} => {data} 0.1333333 1.0000000 2.307692 4
## [1790] {data,
## import} => {show} 0.1000000 0.7500000 1.406250 3
## [1791] {import,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [1792] {data,
## import} => {model} 0.1333333 1.0000000 1.875000 4
## [1793] {import,
## model} => {data} 0.1333333 1.0000000 2.307692 4
## [1794] {data,
## import} => {featur} 0.1333333 1.0000000 1.875000 4
## [1795] {featur,
## import} => {data} 0.1333333 1.0000000 2.307692 4
## [1796] {import,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [1797] {import,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [1798] {import,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [1799] {import,
## model} => {learn} 0.1000000 0.7500000 1.730769 3
## [1800] {import,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [1801] {featur,
## import} => {learn} 0.1000000 0.7500000 1.730769 3
## [1802] {import,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [1803] {import,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [1804] {import,
## represent} => {model} 0.1333333 1.0000000 1.875000 4
## [1805] {import,
## model} => {represent} 0.1333333 1.0000000 2.000000 4
## [1806] {import,
## represent} => {featur} 0.1333333 1.0000000 1.875000 4
## [1807] {featur,
## import} => {represent} 0.1333333 1.0000000 2.000000 4
## [1808] {import,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [1809] {import,
## model} => {show} 0.1000000 0.7500000 1.406250 3
## [1810] {import,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [1811] {featur,
## import} => {show} 0.1000000 0.7500000 1.406250 3
## [1812] {import,
## model} => {featur} 0.1333333 1.0000000 1.875000 4
## [1813] {featur,
## import} => {model} 0.1333333 1.0000000 1.875000 4
## [1814] {art,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [1815] {problem,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [1816] {problem,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [1817] {art,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [1818] {task,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [1819] {task,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [1820] {art,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [1821] {perform,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [1822] {perform,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [1823] {art,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [1824] {data,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [1825] {data,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [1826] {art,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1827] {dataset,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [1828] {dataset,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [1829] {art,
## state} => {learn} 0.1333333 1.0000000 2.307692 4
## [1830] {learn,
## art} => {state} 0.1333333 1.0000000 7.500000 4
## [1831] {learn,
## state} => {art} 0.1333333 1.0000000 7.500000 4
## [1832] {art,
## state} => {propos} 0.1333333 1.0000000 2.000000 4
## [1833] {propos,
## art} => {state} 0.1333333 1.0000000 7.500000 4
## [1834] {propos,
## state} => {art} 0.1333333 1.0000000 7.500000 4
## [1835] {art,
## state} => {model} 0.1333333 1.0000000 1.875000 4
## [1836] {model,
## art} => {state} 0.1333333 1.0000000 7.500000 4
## [1837] {model,
## state} => {art} 0.1333333 1.0000000 7.500000 4
## [1838] {art,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [1839] {featur,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [1840] {featur,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [1841] {problem,
## art} => {perform} 0.1000000 1.0000000 2.142857 3
## [1842] {perform,
## art} => {problem} 0.1000000 1.0000000 3.333333 3
## [1843] {problem,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [1844] {learn,
## art} => {problem} 0.1000000 0.7500000 2.500000 3
## [1845] {problem,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [1846] {propos,
## art} => {problem} 0.1000000 0.7500000 2.500000 3
## [1847] {problem,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [1848] {model,
## art} => {problem} 0.1000000 0.7500000 2.500000 3
## [1849] {task,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [1850] {data,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [1851] {task,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [1852] {learn,
## art} => {task} 0.1000000 0.7500000 2.045455 3
## [1853] {task,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [1854] {propos,
## art} => {task} 0.1000000 0.7500000 2.045455 3
## [1855] {task,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [1856] {model,
## art} => {task} 0.1000000 0.7500000 2.045455 3
## [1857] {task,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [1858] {featur,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [1859] {perform,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [1860] {learn,
## art} => {perform} 0.1000000 0.7500000 1.607143 3
## [1861] {perform,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [1862] {propos,
## art} => {perform} 0.1000000 0.7500000 1.607143 3
## [1863] {perform,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [1864] {model,
## art} => {perform} 0.1000000 0.7500000 1.607143 3
## [1865] {data,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [1866] {learn,
## art} => {data} 0.1000000 0.7500000 1.730769 3
## [1867] {data,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [1868] {propos,
## art} => {data} 0.1000000 0.7500000 1.730769 3
## [1869] {data,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [1870] {model,
## art} => {data} 0.1000000 0.7500000 1.730769 3
## [1871] {data,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [1872] {featur,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [1873] {dataset,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [1874] {learn,
## art} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1875] {dataset,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [1876] {propos,
## art} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1877] {dataset,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [1878] {model,
## art} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1879] {learn,
## art} => {propos} 0.1333333 1.0000000 2.000000 4
## [1880] {propos,
## art} => {learn} 0.1333333 1.0000000 2.307692 4
## [1881] {learn,
## art} => {model} 0.1333333 1.0000000 1.875000 4
## [1882] {model,
## art} => {learn} 0.1333333 1.0000000 2.307692 4
## [1883] {learn,
## art} => {featur} 0.1000000 0.7500000 1.406250 3
## [1884] {featur,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [1885] {propos,
## art} => {model} 0.1333333 1.0000000 1.875000 4
## [1886] {model,
## art} => {propos} 0.1333333 1.0000000 2.000000 4
## [1887] {propos,
## art} => {featur} 0.1000000 0.7500000 1.406250 3
## [1888] {featur,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [1889] {model,
## art} => {featur} 0.1000000 0.7500000 1.406250 3
## [1890] {featur,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [1891] {problem,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [1892] {perform,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [1893] {problem,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [1894] {learn,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [1895] {problem,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [1896] {propos,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [1897] {problem,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [1898] {model,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [1899] {task,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [1900] {data,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [1901] {task,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [1902] {learn,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [1903] {task,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [1904] {propos,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [1905] {task,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [1906] {model,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [1907] {task,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [1908] {featur,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [1909] {perform,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [1910] {learn,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [1911] {perform,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [1912] {propos,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [1913] {perform,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [1914] {model,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [1915] {data,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [1916] {learn,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [1917] {data,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [1918] {propos,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [1919] {data,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [1920] {model,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [1921] {data,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [1922] {featur,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [1923] {dataset,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [1924] {learn,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1925] {dataset,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [1926] {propos,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1927] {dataset,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [1928] {model,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1929] {learn,
## state} => {propos} 0.1333333 1.0000000 2.000000 4
## [1930] {propos,
## state} => {learn} 0.1333333 1.0000000 2.307692 4
## [1931] {learn,
## state} => {model} 0.1333333 1.0000000 1.875000 4
## [1932] {model,
## state} => {learn} 0.1333333 1.0000000 2.307692 4
## [1933] {learn,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [1934] {featur,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [1935] {propos,
## state} => {model} 0.1333333 1.0000000 1.875000 4
## [1936] {model,
## state} => {propos} 0.1333333 1.0000000 2.000000 4
## [1937] {propos,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [1938] {featur,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [1939] {model,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [1940] {featur,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [1941] {general,
## joint} => {perform} 0.1000000 1.0000000 2.142857 3
## [1942] {joint,
## perform} => {general} 0.1000000 1.0000000 5.000000 3
## [1943] {general,
## perform} => {joint} 0.1000000 1.0000000 7.500000 3
## [1944] {general,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [1945] {show,
## joint} => {general} 0.1000000 0.7500000 3.750000 3
## [1946] {general,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [1947] {joint,
## propos} => {general} 0.1000000 0.7500000 3.750000 3
## [1948] {general,
## propos} => {joint} 0.1000000 0.7500000 5.625000 3
## [1949] {joint,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [1950] {show,
## joint} => {object} 0.1000000 0.7500000 2.812500 3
## [1951] {joint,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [1952] {joint,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [1953] {method,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [1954] {approach,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [1955] {method,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1956] {dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [1957] {method,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [1958] {show,
## joint} => {method} 0.1000000 0.7500000 2.045455 3
## [1959] {method,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [1960] {joint,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [1961] {method,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [1962] {model,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [1963] {approach,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1964] {dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [1965] {approach,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [1966] {show,
## joint} => {approach} 0.1000000 0.7500000 1.875000 3
## [1967] {approach,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [1968] {joint,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [1969] {approach,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [1970] {model,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [1971] {joint,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [1972] {show,
## joint} => {perform} 0.1000000 0.7500000 1.607143 3
## [1973] {joint,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [1974] {joint,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [1975] {dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [1976] {show,
## joint} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1977] {dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [1978] {joint,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1979] {dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [1980] {model,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1981] {represent,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [1982] {show,
## joint} => {represent} 0.1000000 0.7500000 1.500000 3
## [1983] {represent,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [1984] {joint,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [1985] {show,
## joint} => {propos} 0.1333333 1.0000000 2.000000 4
## [1986] {joint,
## propos} => {show} 0.1333333 1.0000000 1.875000 4
## [1987] {show,
## joint} => {model} 0.1000000 0.7500000 1.406250 3
## [1988] {model,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [1989] {joint,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [1990] {model,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [1991] {addit,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [1992] {imag,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [1993] {addit,
## imag} => {studi} 0.1000000 1.0000000 7.500000 3
## [1994] {addit,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [1995] {classif,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [1996] {classif,
## addit} => {studi} 0.1000000 1.0000000 7.500000 3
## [1997] {addit,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [1998] {propos,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [1999] {imag,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [2000] {classif,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [2001] {classif,
## imag} => {studi} 0.1000000 1.0000000 7.500000 3
## [2002] {imag,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [2003] {propos,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [2004] {process,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [2005] {studi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [2006] {process,
## work} => {studi} 0.1000000 0.7500000 5.625000 3
## [2007] {process,
## studi} => {network} 0.1000000 1.0000000 1.578947 3
## [2008] {network,
## studi} => {process} 0.1000000 1.0000000 5.000000 3
## [2009] {classif,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [2010] {propos,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [2011] {studi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [2012] {network,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [2013] {studi,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [2014] {featur,
## studi} => {learn} 0.1000000 1.0000000 2.307692 3
## [2015] {evalu,
## make} => {paper} 0.1000000 1.0000000 3.000000 3
## [2016] {evalu,
## paper} => {make} 0.1000000 0.7500000 2.500000 3
## [2017] {evalu,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [2018] {evalu,
## method} => {make} 0.1000000 0.7500000 2.500000 3
## [2019] {evalu,
## paper} => {method} 0.1000000 0.7500000 2.045455 3
## [2020] {evalu,
## method} => {paper} 0.1000000 0.7500000 2.250000 3
## [2021] {method,
## paper} => {evalu} 0.1000000 0.7500000 4.500000 3
## [2022] {evalu,
## paper} => {represent} 0.1000000 0.7500000 1.500000 3
## [2023] {evalu,
## represent} => {paper} 0.1000000 0.7500000 2.250000 3
## [2024] {evalu,
## paper} => {show} 0.1000000 0.7500000 1.406250 3
## [2025] {evalu,
## show} => {paper} 0.1000000 0.7500000 2.250000 3
## [2026] {evalu,
## paper} => {model} 0.1000000 0.7500000 1.406250 3
## [2027] {evalu,
## model} => {paper} 0.1000000 0.7500000 2.250000 3
## [2028] {evalu,
## paper} => {network} 0.1000000 0.7500000 1.184211 3
## [2029] {evalu,
## network} => {paper} 0.1000000 0.7500000 2.250000 3
## [2030] {evalu,
## method} => {task} 0.1000000 0.7500000 2.045455 3
## [2031] {evalu,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [2032] {method,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [2033] {evalu,
## method} => {approach} 0.1000000 0.7500000 1.875000 3
## [2034] {approach,
## evalu} => {method} 0.1000000 1.0000000 2.727273 3
## [2035] {evalu,
## method} => {data} 0.1000000 0.7500000 1.730769 3
## [2036] {data,
## evalu} => {method} 0.1000000 1.0000000 2.727273 3
## [2037] {data,
## method} => {evalu} 0.1000000 1.0000000 6.000000 3
## [2038] {evalu,
## method} => {represent} 0.1000000 0.7500000 1.500000 3
## [2039] {evalu,
## represent} => {method} 0.1000000 0.7500000 2.045455 3
## [2040] {evalu,
## method} => {show} 0.1000000 0.7500000 1.406250 3
## [2041] {evalu,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [2042] {evalu,
## method} => {model} 0.1000000 0.7500000 1.406250 3
## [2043] {evalu,
## model} => {method} 0.1000000 0.7500000 2.045455 3
## [2044] {evalu,
## method} => {featur} 0.1000000 0.7500000 1.406250 3
## [2045] {evalu,
## featur} => {method} 0.1000000 1.0000000 2.727273 3
## [2046] {evalu,
## method} => {network} 0.1000000 0.7500000 1.184211 3
## [2047] {evalu,
## network} => {method} 0.1000000 0.7500000 2.045455 3
## [2048] {evalu,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [2049] {approach,
## evalu} => {task} 0.1000000 1.0000000 2.727273 3
## [2050] {evalu,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [2051] {evalu,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [2052] {evalu,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [2053] {evalu,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [2054] {evalu,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [2055] {evalu,
## network} => {task} 0.1000000 0.7500000 2.045455 3
## [2056] {approach,
## evalu} => {represent} 0.1000000 1.0000000 2.000000 3
## [2057] {evalu,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [2058] {approach,
## evalu} => {featur} 0.1000000 1.0000000 1.875000 3
## [2059] {evalu,
## featur} => {approach} 0.1000000 1.0000000 2.500000 3
## [2060] {approach,
## evalu} => {network} 0.1000000 1.0000000 1.578947 3
## [2061] {evalu,
## network} => {approach} 0.1000000 0.7500000 1.875000 3
## [2062] {data,
## evalu} => {show} 0.1000000 1.0000000 1.875000 3
## [2063] {evalu,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [2064] {data,
## evalu} => {model} 0.1000000 1.0000000 1.875000 3
## [2065] {evalu,
## model} => {data} 0.1000000 0.7500000 1.730769 3
## [2066] {evalu,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [2067] {evalu,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [2068] {evalu,
## represent} => {model} 0.1000000 0.7500000 1.406250 3
## [2069] {evalu,
## model} => {represent} 0.1000000 0.7500000 1.500000 3
## [2070] {evalu,
## represent} => {featur} 0.1000000 0.7500000 1.406250 3
## [2071] {evalu,
## featur} => {represent} 0.1000000 1.0000000 2.000000 3
## [2072] {evalu,
## represent} => {network} 0.1333333 1.0000000 1.578947 4
## [2073] {evalu,
## network} => {represent} 0.1333333 1.0000000 2.000000 4
## [2074] {evalu,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [2075] {evalu,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [2076] {evalu,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [2077] {evalu,
## network} => {show} 0.1000000 0.7500000 1.406250 3
## [2078] {evalu,
## model} => {network} 0.1000000 0.7500000 1.184211 3
## [2079] {evalu,
## network} => {model} 0.1000000 0.7500000 1.406250 3
## [2080] {evalu,
## featur} => {network} 0.1000000 1.0000000 1.578947 3
## [2081] {evalu,
## network} => {featur} 0.1000000 0.7500000 1.406250 3
## [2082] {outperform,
## challeng} => {object} 0.1000000 1.0000000 3.750000 3
## [2083] {outperform,
## object} => {challeng} 0.1000000 1.0000000 6.000000 3
## [2084] {object,
## challeng} => {outperform} 0.1000000 1.0000000 7.500000 3
## [2085] {outperform,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [2086] {outperform,
## show} => {challeng} 0.1000000 0.7500000 4.500000 3
## [2087] {show,
## challeng} => {outperform} 0.1000000 0.7500000 5.625000 3
## [2088] {outperform,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [2089] {outperform,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [2090] {propos,
## challeng} => {outperform} 0.1000000 0.7500000 5.625000 3
## [2091] {outperform,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [2092] {outperform,
## show} => {object} 0.1000000 0.7500000 2.812500 3
## [2093] {outperform,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [2094] {outperform,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [2095] {outperform,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [2096] {data,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [2097] {outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [2098] {outperform,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [2099] {outperform,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [2100] {model,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [2101] {outperform,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [2102] {featur,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [2103] {outperform,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [2104] {outperform,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [2105] {data,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [2106] {outperform,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [2107] {data,
## outperform} => {model} 0.1000000 1.0000000 1.875000 3
## [2108] {model,
## outperform} => {data} 0.1000000 1.0000000 2.307692 3
## [2109] {data,
## outperform} => {featur} 0.1000000 1.0000000 1.875000 3
## [2110] {featur,
## outperform} => {data} 0.1000000 1.0000000 2.307692 3
## [2111] {outperform,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [2112] {outperform,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [2113] {outperform,
## show} => {propos} 0.1000000 0.7500000 1.500000 3
## [2114] {outperform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [2115] {outperform,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [2116] {model,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [2117] {outperform,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [2118] {featur,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [2119] {model,
## outperform} => {featur} 0.1000000 1.0000000 1.875000 3
## [2120] {featur,
## outperform} => {model} 0.1000000 1.0000000 1.875000 3
## [2121] {task,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [2122] {data,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [2123] {task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2124] {dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [2125] {task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [2126] {learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [2127] {task,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [2128] {featur,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [2129] {task,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2130] {network,
## design} => {task} 0.1000000 0.7500000 2.045455 3
## [2131] {work,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2132] {network,
## design} => {work} 0.1000000 0.7500000 1.875000 3
## [2133] {data,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2134] {dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [2135] {data,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [2136] {learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [2137] {data,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [2138] {featur,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [2139] {data,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2140] {network,
## design} => {data} 0.1000000 0.7500000 1.730769 3
## [2141] {dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [2142] {learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2143] {dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [2144] {featur,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2145] {dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2146] {network,
## design} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2147] {learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [2148] {featur,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [2149] {learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2150] {network,
## design} => {learn} 0.1000000 0.7500000 1.730769 3
## [2151] {featur,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2152] {network,
## design} => {featur} 0.1000000 0.7500000 1.406250 3
## [2153] {general,
## system} => {show} 0.1000000 1.0000000 1.875000 3
## [2154] {show,
## system} => {general} 0.1000000 1.0000000 5.000000 3
## [2155] {general,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [2156] {model,
## system} => {general} 0.1000000 0.7500000 3.750000 3
## [2157] {model,
## general} => {system} 0.1000000 0.7500000 4.500000 3
## [2158] {architectur,
## system} => {featur} 0.1000000 1.0000000 1.875000 3
## [2159] {featur,
## system} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2160] {recognit,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [2161] {model,
## system} => {recognit} 0.1000000 0.7500000 2.500000 3
## [2162] {model,
## recognit} => {system} 0.1000000 0.7500000 4.500000 3
## [2163] {recognit,
## system} => {featur} 0.1000000 1.0000000 1.875000 3
## [2164] {featur,
## system} => {recognit} 0.1000000 0.7500000 2.500000 3
## [2165] {method,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [2166] {perform,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [2167] {method,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2168] {dataset,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [2169] {method,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [2170] {propos,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [2171] {perform,
## system} => {dataset} 0.1333333 1.0000000 2.307692 4
## [2172] {dataset,
## system} => {perform} 0.1333333 1.0000000 2.142857 4
## [2173] {perform,
## system} => {propos} 0.1333333 1.0000000 2.000000 4
## [2174] {propos,
## system} => {perform} 0.1333333 1.0000000 2.142857 4
## [2175] {perform,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [2176] {model,
## system} => {perform} 0.1000000 0.7500000 1.607143 3
## [2177] {perform,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [2178] {featur,
## system} => {perform} 0.1000000 0.7500000 1.607143 3
## [2179] {dataset,
## system} => {propos} 0.1333333 1.0000000 2.000000 4
## [2180] {propos,
## system} => {dataset} 0.1333333 1.0000000 2.307692 4
## [2181] {dataset,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [2182] {model,
## system} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2183] {dataset,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [2184] {featur,
## system} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2185] {system,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [2186] {represent,
## system} => {learn} 0.1000000 1.0000000 2.307692 3
## [2187] {system,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [2188] {model,
## system} => {learn} 0.1000000 0.7500000 1.730769 3
## [2189] {represent,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [2190] {model,
## system} => {represent} 0.1000000 0.7500000 1.500000 3
## [2191] {show,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [2192] {model,
## system} => {show} 0.1000000 0.7500000 1.406250 3
## [2193] {propos,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [2194] {model,
## system} => {propos} 0.1000000 0.7500000 1.500000 3
## [2195] {propos,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [2196] {featur,
## system} => {propos} 0.1000000 0.7500000 1.500000 3
## [2197] {model,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [2198] {featur,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [2199] {detect,
## stateoftheart} => {method} 0.1000000 1.0000000 2.727273 3
## [2200] {method,
## detect} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [2201] {method,
## stateoftheart} => {detect} 0.1000000 1.0000000 6.000000 3
## [2202] {detect,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2203] {detect,
## propos} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [2204] {detect,
## stateoftheart} => {featur} 0.1000000 1.0000000 1.875000 3
## [2205] {featur,
## stateoftheart} => {detect} 0.1000000 1.0000000 6.000000 3
## [2206] {appli,
## detect} => {method} 0.1000000 1.0000000 2.727273 3
## [2207] {method,
## detect} => {appli} 0.1000000 0.7500000 3.750000 3
## [2208] {method,
## appli} => {detect} 0.1000000 0.7500000 4.500000 3
## [2209] {appli,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [2210] {detect,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [2211] {appli,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [2212] {detect,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [2213] {appli,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [2214] {featur,
## appli} => {detect} 0.1000000 0.7500000 4.500000 3
## [2215] {detect,
## object} => {method} 0.1000000 1.0000000 2.727273 3
## [2216] {method,
## detect} => {object} 0.1000000 0.7500000 2.812500 3
## [2217] {method,
## object} => {detect} 0.1000000 0.7500000 4.500000 3
## [2218] {detect,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [2219] {show,
## detect} => {object} 0.1000000 1.0000000 3.750000 3
## [2220] {detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [2221] {detect,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [2222] {detect,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [2223] {architectur,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [2224] {detect,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2225] {architectur,
## detect} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2226] {dataset,
## detect} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2227] {architectur,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [2228] {architectur,
## detect} => {network} 0.1000000 1.0000000 1.578947 3
## [2229] {network,
## detect} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2230] {method,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [2231] {detect,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [2232] {method,
## detect} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2233] {dataset,
## detect} => {method} 0.1000000 0.7500000 2.045455 3
## [2234] {method,
## detect} => {show} 0.1000000 0.7500000 1.406250 3
## [2235] {show,
## detect} => {method} 0.1000000 1.0000000 2.727273 3
## [2236] {method,
## detect} => {propos} 0.1333333 1.0000000 2.000000 4
## [2237] {detect,
## propos} => {method} 0.1333333 1.0000000 2.727273 4
## [2238] {method,
## detect} => {featur} 0.1333333 1.0000000 1.875000 4
## [2239] {featur,
## detect} => {method} 0.1333333 0.8000000 2.181818 4
## [2240] {method,
## detect} => {network} 0.1000000 0.7500000 1.184211 3
## [2241] {network,
## detect} => {method} 0.1000000 0.7500000 2.045455 3
## [2242] {detect,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2243] {dataset,
## detect} => {work} 0.1000000 0.7500000 1.875000 3
## [2244] {detect,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [2245] {detect,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [2246] {network,
## detect} => {work} 0.1000000 0.7500000 1.875000 3
## [2247] {detect,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2248] {dataset,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [2249] {detect,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [2250] {detect,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [2251] {detect,
## perform} => {featur} 0.1333333 1.0000000 1.875000 4
## [2252] {featur,
## detect} => {perform} 0.1333333 0.8000000 1.714286 4
## [2253] {detect,
## perform} => {network} 0.1000000 0.7500000 1.184211 3
## [2254] {network,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [2255] {dataset,
## detect} => {propos} 0.1000000 0.7500000 1.500000 3
## [2256] {detect,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2257] {dataset,
## detect} => {featur} 0.1333333 1.0000000 1.875000 4
## [2258] {featur,
## detect} => {dataset} 0.1333333 0.8000000 1.846154 4
## [2259] {dataset,
## detect} => {network} 0.1333333 1.0000000 1.578947 4
## [2260] {network,
## detect} => {dataset} 0.1333333 1.0000000 2.307692 4
## [2261] {represent,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [2262] {show,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [2263] {detect,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [2264] {show,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [2265] {detect,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [2266] {featur,
## detect} => {propos} 0.1333333 0.8000000 1.600000 4
## [2267] {detect,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [2268] {network,
## detect} => {propos} 0.1000000 0.7500000 1.500000 3
## [2269] {featur,
## detect} => {network} 0.1333333 0.8000000 1.263158 4
## [2270] {network,
## detect} => {featur} 0.1333333 1.0000000 1.875000 4
## [2271] {object,
## stateoftheart} => {represent} 0.1000000 1.0000000 2.000000 3
## [2272] {represent,
## stateoftheart} => {object} 0.1000000 1.0000000 3.750000 3
## [2273] {represent,
## object} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [2274] {object,
## stateoftheart} => {show} 0.1000000 1.0000000 1.875000 3
## [2275] {show,
## stateoftheart} => {object} 0.1000000 1.0000000 3.750000 3
## [2276] {object,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2277] {method,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2278] {method,
## stateoftheart} => {featur} 0.1000000 1.0000000 1.875000 3
## [2279] {featur,
## stateoftheart} => {method} 0.1000000 1.0000000 2.727273 3
## [2280] {work,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2281] {perform,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2282] {dataset,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2283] {dataset,
## stateoftheart} => {network} 0.1000000 1.0000000 1.578947 3
## [2284] {network,
## stateoftheart} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2285] {represent,
## stateoftheart} => {show} 0.1000000 1.0000000 1.875000 3
## [2286] {show,
## stateoftheart} => {represent} 0.1000000 1.0000000 2.000000 3
## [2287] {represent,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2288] {show,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2289] {featur,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2290] {network,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2291] {addit,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2292] {architectur,
## effici} => {addit} 0.1000000 0.7500000 4.500000 3
## [2293] {architectur,
## addit} => {effici} 0.1000000 1.0000000 6.000000 3
## [2294] {addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [2295] {effici,
## work} => {addit} 0.1000000 0.7500000 4.500000 3
## [2296] {addit,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [2297] {addit,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2298] {dataset,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [2299] {dataset,
## addit} => {effici} 0.1000000 0.7500000 4.500000 3
## [2300] {addit,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [2301] {propos,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [2302] {addit,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [2303] {network,
## effici} => {addit} 0.1000000 0.7500000 4.500000 3
## [2304] {network,
## addit} => {effici} 0.1000000 0.7500000 4.500000 3
## [2305] {comput,
## effici} => {reduc} 0.1000000 1.0000000 4.285714 3
## [2306] {reduc,
## effici} => {comput} 0.1000000 1.0000000 4.285714 3
## [2307] {reduc,
## comput} => {effici} 0.1000000 0.7500000 4.500000 3
## [2308] {comput,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [2309] {effici,
## optim} => {comput} 0.1000000 1.0000000 4.285714 3
## [2310] {comput,
## optim} => {effici} 0.1000000 1.0000000 6.000000 3
## [2311] {comput,
## effici} => {problem} 0.1000000 1.0000000 3.333333 3
## [2312] {effici,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [2313] {comput,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [2314] {comput,
## effici} => {improv} 0.1000000 1.0000000 3.333333 3
## [2315] {improv,
## effici} => {comput} 0.1000000 1.0000000 4.285714 3
## [2316] {improv,
## comput} => {effici} 0.1000000 0.7500000 4.500000 3
## [2317] {reduc,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [2318] {effici,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [2319] {reduc,
## optim} => {effici} 0.1000000 0.7500000 4.500000 3
## [2320] {reduc,
## effici} => {problem} 0.1000000 1.0000000 3.333333 3
## [2321] {effici,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [2322] {reduc,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [2323] {reduc,
## effici} => {improv} 0.1000000 1.0000000 3.333333 3
## [2324] {improv,
## effici} => {reduc} 0.1000000 1.0000000 4.285714 3
## [2325] {reduc,
## improv} => {effici} 0.1000000 1.0000000 6.000000 3
## [2326] {effici,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [2327] {effici,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [2328] {effici,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [2329] {improv,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [2330] {improv,
## optim} => {effici} 0.1000000 0.7500000 4.500000 3
## [2331] {architectur,
## effici} => {work} 0.1333333 1.0000000 2.500000 4
## [2332] {effici,
## work} => {architectur} 0.1333333 1.0000000 3.750000 4
## [2333] {architectur,
## work} => {effici} 0.1333333 0.8000000 4.800000 4
## [2334] {architectur,
## effici} => {perform} 0.1000000 0.7500000 1.607143 3
## [2335] {perform,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2336] {architectur,
## effici} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2337] {dataset,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2338] {architectur,
## effici} => {propos} 0.1000000 0.7500000 1.500000 3
## [2339] {propos,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2340] {architectur,
## effici} => {network} 0.1333333 1.0000000 1.578947 4
## [2341] {network,
## effici} => {architectur} 0.1333333 1.0000000 3.750000 4
## [2342] {effici,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [2343] {improv,
## effici} => {problem} 0.1000000 1.0000000 3.333333 3
## [2344] {improv,
## problem} => {effici} 0.1000000 0.7500000 4.500000 3
## [2345] {effici,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [2346] {perform,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [2347] {effici,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2348] {dataset,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [2349] {effici,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [2350] {propos,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [2351] {effici,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [2352] {network,
## effici} => {work} 0.1333333 1.0000000 2.500000 4
## [2353] {perform,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [2354] {network,
## effici} => {perform} 0.1000000 0.7500000 1.607143 3
## [2355] {dataset,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [2356] {propos,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2357] {dataset,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [2358] {network,
## effici} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2359] {propos,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [2360] {network,
## effici} => {propos} 0.1000000 0.7500000 1.500000 3
## [2361] {addit,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [2362] {set,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [2363] {addit,
## work} => {set} 0.1000000 0.7500000 5.625000 3
## [2364] {addit,
## set} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2365] {dataset,
## set} => {addit} 0.1000000 1.0000000 6.000000 3
## [2366] {dataset,
## addit} => {set} 0.1000000 0.7500000 5.625000 3
## [2367] {addit,
## set} => {propos} 0.1000000 1.0000000 2.000000 3
## [2368] {propos,
## set} => {addit} 0.1000000 1.0000000 6.000000 3
## [2369] {addit,
## set} => {network} 0.1000000 1.0000000 1.578947 3
## [2370] {network,
## set} => {addit} 0.1000000 1.0000000 6.000000 3
## [2371] {network,
## addit} => {set} 0.1000000 0.7500000 5.625000 3
## [2372] {process,
## set} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2373] {architectur,
## set} => {process} 0.1000000 1.0000000 5.000000 3
## [2374] {recognit,
## set} => {task} 0.1000000 1.0000000 2.727273 3
## [2375] {task,
## set} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2376] {task,
## recognit} => {set} 0.1000000 0.7500000 5.625000 3
## [2377] {recognit,
## set} => {data} 0.1000000 1.0000000 2.307692 3
## [2378] {data,
## set} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2379] {recognit,
## set} => {learn} 0.1000000 1.0000000 2.307692 3
## [2380] {set,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2381] {task,
## set} => {data} 0.1000000 1.0000000 2.307692 3
## [2382] {data,
## set} => {task} 0.1000000 1.0000000 2.727273 3
## [2383] {task,
## set} => {learn} 0.1000000 1.0000000 2.307692 3
## [2384] {set,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [2385] {set,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2386] {dataset,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [2387] {set,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [2388] {propos,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [2389] {set,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [2390] {network,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [2391] {data,
## set} => {learn} 0.1000000 1.0000000 2.307692 3
## [2392] {set,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [2393] {dataset,
## set} => {propos} 0.1000000 1.0000000 2.000000 3
## [2394] {propos,
## set} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2395] {dataset,
## set} => {network} 0.1000000 1.0000000 1.578947 3
## [2396] {network,
## set} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2397] {propos,
## set} => {network} 0.1000000 1.0000000 1.578947 3
## [2398] {network,
## set} => {propos} 0.1000000 1.0000000 2.000000 3
## [2399] {shallow,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [2400] {featur,
## shallow} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [2401] {featur,
## techniqu} => {shallow} 0.1000000 0.7500000 4.500000 3
## [2402] {architectur,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [2403] {result,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2404] {architectur,
## result} => {shallow} 0.1000000 0.7500000 4.500000 3
## [2405] {architectur,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [2406] {neural,
## shallow} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2407] {architectur,
## neural} => {shallow} 0.1000000 0.7500000 4.500000 3
## [2408] {architectur,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2409] {featur,
## shallow} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2410] {architectur,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [2411] {network,
## shallow} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2412] {classif,
## shallow} => {method} 0.1000000 1.0000000 2.727273 3
## [2413] {method,
## shallow} => {classif} 0.1000000 1.0000000 3.750000 3
## [2414] {classif,
## shallow} => {approach} 0.1000000 1.0000000 2.500000 3
## [2415] {approach,
## shallow} => {classif} 0.1000000 1.0000000 3.750000 3
## [2416] {classif,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2417] {featur,
## shallow} => {classif} 0.1000000 0.7500000 2.812500 3
## [2418] {improv,
## shallow} => {perform} 0.1000000 1.0000000 2.142857 3
## [2419] {perform,
## shallow} => {improv} 0.1000000 1.0000000 3.333333 3
## [2420] {improv,
## shallow} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2421] {dataset,
## shallow} => {improv} 0.1000000 1.0000000 3.333333 3
## [2422] {improv,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2423] {featur,
## shallow} => {improv} 0.1000000 0.7500000 2.500000 3
## [2424] {featur,
## improv} => {shallow} 0.1000000 0.7500000 4.500000 3
## [2425] {result,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [2426] {neural,
## shallow} => {result} 0.1000000 0.7500000 2.250000 3
## [2427] {result,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2428] {featur,
## shallow} => {result} 0.1000000 0.7500000 2.250000 3
## [2429] {result,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [2430] {network,
## shallow} => {result} 0.1000000 0.7500000 2.250000 3
## [2431] {neural,
## shallow} => {featur} 0.1000000 0.7500000 1.406250 3
## [2432] {featur,
## shallow} => {neural} 0.1000000 0.7500000 2.250000 3
## [2433] {neural,
## shallow} => {network} 0.1333333 1.0000000 1.578947 4
## [2434] {network,
## shallow} => {neural} 0.1333333 1.0000000 3.000000 4
## [2435] {method,
## shallow} => {approach} 0.1000000 1.0000000 2.500000 3
## [2436] {approach,
## shallow} => {method} 0.1000000 1.0000000 2.727273 3
## [2437] {method,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2438] {featur,
## shallow} => {method} 0.1000000 0.7500000 2.045455 3
## [2439] {approach,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2440] {featur,
## shallow} => {approach} 0.1000000 0.7500000 1.875000 3
## [2441] {perform,
## shallow} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2442] {dataset,
## shallow} => {perform} 0.1000000 1.0000000 2.142857 3
## [2443] {perform,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2444] {featur,
## shallow} => {perform} 0.1000000 0.7500000 1.607143 3
## [2445] {dataset,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2446] {featur,
## shallow} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2447] {featur,
## shallow} => {network} 0.1000000 0.7500000 1.184211 3
## [2448] {network,
## shallow} => {featur} 0.1000000 0.7500000 1.406250 3
## [2449] {semant,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [2450] {work,
## semant} => {larg} 0.1000000 0.7500000 4.500000 3
## [2451] {semant,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [2452] {represent,
## larg} => {semant} 0.1000000 0.7500000 4.500000 3
## [2453] {semant,
## larg} => {propos} 0.1000000 1.0000000 2.000000 3
## [2454] {propos,
## semant} => {larg} 0.1000000 0.7500000 4.500000 3
## [2455] {propos,
## larg} => {semant} 0.1000000 1.0000000 6.000000 3
## [2456] {task,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [2457] {approach,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [2458] {task,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [2459] {work,
## semant} => {task} 0.1000000 0.7500000 2.045455 3
## [2460] {task,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [2461] {task,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [2462] {data,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [2463] {task,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2464] {dataset,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [2465] {task,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [2466] {learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [2467] {task,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2468] {task,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [2469] {propos,
## semant} => {task} 0.1000000 0.7500000 2.045455 3
## [2470] {approach,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [2471] {work,
## semant} => {approach} 0.1000000 0.7500000 1.875000 3
## [2472] {approach,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [2473] {data,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [2474] {approach,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2475] {dataset,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [2476] {approach,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [2477] {learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [2478] {approach,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2479] {approach,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [2480] {propos,
## semant} => {approach} 0.1000000 0.7500000 1.875000 3
## [2481] {work,
## semant} => {data} 0.1000000 0.7500000 1.730769 3
## [2482] {data,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [2483] {work,
## semant} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2484] {dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [2485] {work,
## semant} => {learn} 0.1000000 0.7500000 1.730769 3
## [2486] {learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [2487] {work,
## semant} => {represent} 0.1333333 1.0000000 2.000000 4
## [2488] {represent,
## semant} => {work} 0.1333333 0.8000000 2.000000 4
## [2489] {work,
## semant} => {show} 0.1000000 0.7500000 1.406250 3
## [2490] {show,
## semant} => {work} 0.1000000 0.7500000 1.875000 3
## [2491] {work,
## semant} => {propos} 0.1333333 1.0000000 2.000000 4
## [2492] {propos,
## semant} => {work} 0.1333333 1.0000000 2.500000 4
## [2493] {data,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2494] {dataset,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [2495] {data,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [2496] {learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [2497] {data,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2498] {data,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [2499] {propos,
## semant} => {data} 0.1000000 0.7500000 1.730769 3
## [2500] {dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [2501] {learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2502] {dataset,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2503] {dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [2504] {propos,
## semant} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2505] {learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2506] {learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [2507] {propos,
## semant} => {learn} 0.1000000 0.7500000 1.730769 3
## [2508] {represent,
## semant} => {show} 0.1333333 0.8000000 1.500000 4
## [2509] {show,
## semant} => {represent} 0.1333333 1.0000000 2.000000 4
## [2510] {represent,
## semant} => {propos} 0.1333333 0.8000000 1.600000 4
## [2511] {propos,
## semant} => {represent} 0.1333333 1.0000000 2.000000 4
## [2512] {model,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2513] {network,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2514] {show,
## semant} => {propos} 0.1000000 0.7500000 1.500000 3
## [2515] {propos,
## semant} => {show} 0.1000000 0.7500000 1.406250 3
## [2516] {show,
## semant} => {network} 0.1000000 0.7500000 1.184211 3
## [2517] {network,
## semant} => {show} 0.1000000 1.0000000 1.875000 3
## [2518] {process,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2519] {architectur,
## analysi} => {process} 0.1000000 0.7500000 3.750000 3
## [2520] {process,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [2521] {analysi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [2522] {process,
## work} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2523] {process,
## analysi} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2524] {dataset,
## analysi} => {process} 0.1000000 1.0000000 5.000000 3
## [2525] {dataset,
## process} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2526] {process,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2527] {network,
## analysi} => {process} 0.1000000 0.7500000 3.750000 3
## [2528] {experi,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2529] {architectur,
## analysi} => {experi} 0.1000000 0.7500000 2.812500 3
## [2530] {architectur,
## experi} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2531] {experi,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [2532] {classif,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [2533] {classif,
## experi} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2534] {experi,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [2535] {propos,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [2536] {experi,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2537] {network,
## analysi} => {experi} 0.1000000 0.7500000 2.812500 3
## [2538] {architectur,
## analysi} => {classif} 0.1000000 0.7500000 2.812500 3
## [2539] {classif,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2540] {classif,
## architectur} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2541] {architectur,
## analysi} => {neural} 0.1000000 0.7500000 2.250000 3
## [2542] {neural,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2543] {architectur,
## neural} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2544] {architectur,
## analysi} => {train} 0.1000000 0.7500000 1.875000 3
## [2545] {train,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2546] {train,
## architectur} => {analysi} 0.1000000 1.0000000 7.500000 3
## [2547] {architectur,
## analysi} => {work} 0.1000000 0.7500000 1.875000 3
## [2548] {analysi,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2549] {architectur,
## analysi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2550] {dataset,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2551] {architectur,
## analysi} => {propos} 0.1000000 0.7500000 1.500000 3
## [2552] {propos,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2553] {architectur,
## analysi} => {featur} 0.1000000 0.7500000 1.406250 3
## [2554] {featur,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2555] {architectur,
## analysi} => {network} 0.1333333 1.0000000 1.578947 4
## [2556] {network,
## analysi} => {architectur} 0.1333333 1.0000000 3.750000 4
## [2557] {classif,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [2558] {propos,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [2559] {classif,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2560] {network,
## analysi} => {classif} 0.1000000 0.7500000 2.812500 3
## [2561] {neural,
## analysi} => {train} 0.1000000 1.0000000 2.500000 3
## [2562] {train,
## analysi} => {neural} 0.1000000 1.0000000 3.000000 3
## [2563] {neural,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2564] {network,
## analysi} => {neural} 0.1000000 0.7500000 2.250000 3
## [2565] {train,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2566] {network,
## analysi} => {train} 0.1000000 0.7500000 1.875000 3
## [2567] {analysi,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2568] {dataset,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [2569] {analysi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [2570] {network,
## analysi} => {work} 0.1000000 0.7500000 1.875000 3
## [2571] {dataset,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2572] {network,
## analysi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2573] {propos,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2574] {network,
## analysi} => {propos} 0.1000000 0.7500000 1.500000 3
## [2575] {featur,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2576] {network,
## analysi} => {featur} 0.1000000 0.7500000 1.406250 3
## [2577] {appli,
## addit} => {method} 0.1000000 0.7500000 2.045455 3
## [2578] {method,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [2579] {method,
## appli} => {addit} 0.1000000 0.7500000 4.500000 3
## [2580] {appli,
## addit} => {work} 0.1000000 0.7500000 1.875000 3
## [2581] {addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [2582] {appli,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [2583] {appli,
## addit} => {perform} 0.1000000 0.7500000 1.607143 3
## [2584] {perform,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [2585] {appli,
## addit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2586] {dataset,
## addit} => {appli} 0.1000000 0.7500000 3.750000 3
## [2587] {appli,
## dataset} => {addit} 0.1000000 0.7500000 4.500000 3
## [2588] {appli,
## addit} => {show} 0.1000000 0.7500000 1.406250 3
## [2589] {show,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [2590] {show,
## appli} => {addit} 0.1000000 0.7500000 4.500000 3
## [2591] {appli,
## addit} => {propos} 0.1333333 1.0000000 2.000000 4
## [2592] {propos,
## addit} => {appli} 0.1333333 0.8000000 4.000000 4
## [2593] {appli,
## addit} => {network} 0.1000000 0.7500000 1.184211 3
## [2594] {network,
## addit} => {appli} 0.1000000 0.7500000 3.750000 3
## [2595] {network,
## appli} => {addit} 0.1000000 0.7500000 4.500000 3
## [2596] {addit,
## imag} => {classif} 0.1000000 1.0000000 3.750000 3
## [2597] {classif,
## addit} => {imag} 0.1000000 1.0000000 6.000000 3
## [2598] {classif,
## imag} => {addit} 0.1000000 1.0000000 6.000000 3
## [2599] {addit,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [2600] {architectur,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [2601] {addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2602] {architectur,
## addit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2603] {dataset,
## addit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2604] {architectur,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2605] {architectur,
## addit} => {network} 0.1000000 1.0000000 1.578947 3
## [2606] {network,
## addit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2607] {classif,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2608] {recognit,
## addit} => {learn} 0.1000000 1.0000000 2.307692 3
## [2609] {addit,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2610] {recognit,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2611] {method,
## addit} => {perform} 0.1000000 1.0000000 2.142857 3
## [2612] {perform,
## addit} => {method} 0.1000000 1.0000000 2.727273 3
## [2613] {method,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2614] {addit,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [2615] {dataset,
## addit} => {work} 0.1333333 1.0000000 2.500000 4
## [2616] {addit,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [2617] {propos,
## addit} => {work} 0.1333333 0.8000000 2.000000 4
## [2618] {addit,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [2619] {network,
## addit} => {work} 0.1333333 1.0000000 2.500000 4
## [2620] {perform,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2621] {dataset,
## addit} => {propos} 0.1333333 1.0000000 2.000000 4
## [2622] {propos,
## addit} => {dataset} 0.1333333 0.8000000 1.846154 4
## [2623] {dataset,
## addit} => {network} 0.1333333 1.0000000 1.578947 4
## [2624] {network,
## addit} => {dataset} 0.1333333 1.0000000 2.307692 4
## [2625] {addit,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [2626] {show,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2627] {featur,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2628] {propos,
## addit} => {network} 0.1333333 0.8000000 1.263158 4
## [2629] {network,
## addit} => {propos} 0.1333333 1.0000000 2.000000 4
## [2630] {solv,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [2631] {problem,
## function} => {solv} 0.1000000 0.7500000 3.750000 3
## [2632] {problem,
## solv} => {function} 0.1000000 0.7500000 4.500000 3
## [2633] {recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [2634] {machin,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [2635] {recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [2636] {problem,
## function} => {recent} 0.1000000 0.7500000 3.214286 3
## [2637] {problem,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [2638] {recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [2639] {show,
## function} => {recent} 0.1000000 0.7500000 3.214286 3
## [2640] {recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [2641] {model,
## function} => {recent} 0.1000000 0.7500000 3.214286 3
## [2642] {machin,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [2643] {problem,
## function} => {machin} 0.1000000 0.7500000 3.214286 3
## [2644] {machin,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [2645] {machin,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [2646] {show,
## function} => {machin} 0.1000000 0.7500000 3.214286 3
## [2647] {machin,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [2648] {model,
## function} => {machin} 0.1000000 0.7500000 3.214286 3
## [2649] {optim,
## function} => {object} 0.1333333 1.0000000 3.750000 4
## [2650] {object,
## function} => {optim} 0.1333333 1.0000000 4.285714 4
## [2651] {object,
## optim} => {function} 0.1333333 1.0000000 6.000000 4
## [2652] {optim,
## function} => {problem} 0.1000000 0.7500000 2.500000 3
## [2653] {problem,
## function} => {optim} 0.1000000 0.7500000 3.214286 3
## [2654] {optim,
## function} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [2655] {algorithm,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [2656] {optim,
## function} => {task} 0.1000000 0.7500000 2.045455 3
## [2657] {task,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [2658] {task,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [2659] {optim,
## function} => {data} 0.1000000 0.7500000 1.730769 3
## [2660] {data,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [2661] {data,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [2662] {optim,
## function} => {show} 0.1000000 0.7500000 1.406250 3
## [2663] {show,
## function} => {optim} 0.1000000 0.7500000 3.214286 3
## [2664] {show,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [2665] {optim,
## function} => {propos} 0.1000000 0.7500000 1.500000 3
## [2666] {propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [2667] {propos,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [2668] {optim,
## function} => {model} 0.1000000 0.7500000 1.406250 3
## [2669] {model,
## function} => {optim} 0.1000000 0.7500000 3.214286 3
## [2670] {model,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [2671] {optim,
## function} => {featur} 0.1000000 0.7500000 1.406250 3
## [2672] {featur,
## function} => {optim} 0.1000000 0.7500000 3.214286 3
## [2673] {featur,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [2674] {object,
## function} => {problem} 0.1000000 0.7500000 2.500000 3
## [2675] {problem,
## function} => {object} 0.1000000 0.7500000 2.812500 3
## [2676] {object,
## problem} => {function} 0.1000000 0.7500000 4.500000 3
## [2677] {object,
## function} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [2678] {algorithm,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [2679] {algorithm,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [2680] {object,
## function} => {task} 0.1000000 0.7500000 2.045455 3
## [2681] {task,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [2682] {task,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [2683] {object,
## function} => {data} 0.1000000 0.7500000 1.730769 3
## [2684] {data,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [2685] {data,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [2686] {object,
## function} => {show} 0.1000000 0.7500000 1.406250 3
## [2687] {show,
## function} => {object} 0.1000000 0.7500000 2.812500 3
## [2688] {object,
## function} => {propos} 0.1000000 0.7500000 1.500000 3
## [2689] {propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [2690] {object,
## function} => {model} 0.1000000 0.7500000 1.406250 3
## [2691] {model,
## function} => {object} 0.1000000 0.7500000 2.812500 3
## [2692] {object,
## function} => {featur} 0.1000000 0.7500000 1.406250 3
## [2693] {featur,
## function} => {object} 0.1000000 0.7500000 2.812500 3
## [2694] {problem,
## function} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [2695] {algorithm,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [2696] {algorithm,
## problem} => {function} 0.1000000 0.7500000 4.500000 3
## [2697] {problem,
## function} => {perform} 0.1000000 0.7500000 1.607143 3
## [2698] {perform,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [2699] {problem,
## function} => {show} 0.1000000 0.7500000 1.406250 3
## [2700] {show,
## function} => {problem} 0.1000000 0.7500000 2.500000 3
## [2701] {problem,
## function} => {model} 0.1000000 0.7500000 1.406250 3
## [2702] {model,
## function} => {problem} 0.1000000 0.7500000 2.500000 3
## [2703] {problem,
## function} => {featur} 0.1000000 0.7500000 1.406250 3
## [2704] {featur,
## function} => {problem} 0.1000000 0.7500000 2.500000 3
## [2705] {method,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [2706] {show,
## function} => {method} 0.1000000 0.7500000 2.045455 3
## [2707] {method,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [2708] {model,
## function} => {method} 0.1000000 0.7500000 2.045455 3
## [2709] {task,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [2710] {data,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [2711] {task,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [2712] {propos,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [2713] {task,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [2714] {featur,
## function} => {task} 0.1000000 0.7500000 2.045455 3
## [2715] {approach,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [2716] {featur,
## function} => {approach} 0.1000000 0.7500000 1.875000 3
## [2717] {perform,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [2718] {featur,
## function} => {perform} 0.1000000 0.7500000 1.607143 3
## [2719] {data,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [2720] {propos,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [2721] {data,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [2722] {featur,
## function} => {data} 0.1000000 0.7500000 1.730769 3
## [2723] {learn,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [2724] {show,
## function} => {learn} 0.1000000 0.7500000 1.730769 3
## [2725] {learn,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [2726] {model,
## function} => {learn} 0.1000000 0.7500000 1.730769 3
## [2727] {learn,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [2728] {featur,
## function} => {learn} 0.1000000 0.7500000 1.730769 3
## [2729] {show,
## function} => {model} 0.1333333 1.0000000 1.875000 4
## [2730] {model,
## function} => {show} 0.1333333 1.0000000 1.875000 4
## [2731] {show,
## function} => {featur} 0.1000000 0.7500000 1.406250 3
## [2732] {featur,
## function} => {show} 0.1000000 0.7500000 1.406250 3
## [2733] {propos,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [2734] {featur,
## function} => {propos} 0.1000000 0.7500000 1.500000 3
## [2735] {model,
## function} => {featur} 0.1000000 0.7500000 1.406250 3
## [2736] {featur,
## function} => {model} 0.1000000 0.7500000 1.406250 3
## [2737] {accuraci,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [2738] {accuraci,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [2739] {accuraci,
## effect} => {represent} 0.1000000 1.0000000 2.000000 3
## [2740] {represent,
## accuraci} => {effect} 0.1000000 0.7500000 3.214286 3
## [2741] {accuraci,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [2742] {featur,
## effect} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [2743] {accuraci,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [2744] {paper,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [2745] {paper,
## achiev} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [2746] {accuraci,
## achiev} => {recognit} 0.1000000 0.7500000 2.500000 3
## [2747] {accuraci,
## recognit} => {achiev} 0.1000000 0.7500000 3.214286 3
## [2748] {achiev,
## recognit} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [2749] {accuraci,
## achiev} => {result} 0.1000000 0.7500000 2.250000 3
## [2750] {accuraci,
## result} => {achiev} 0.1000000 1.0000000 4.285714 3
## [2751] {achiev,
## result} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [2752] {accuraci,
## achiev} => {neural} 0.1000000 0.7500000 2.250000 3
## [2753] {accuraci,
## neural} => {achiev} 0.1000000 1.0000000 4.285714 3
## [2754] {achiev,
## neural} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [2755] {accuraci,
## achiev} => {train} 0.1000000 0.7500000 1.875000 3
## [2756] {train,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [2757] {train,
## achiev} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [2758] {accuraci,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2759] {accuraci,
## dataset} => {achiev} 0.1000000 1.0000000 4.285714 3
## [2760] {accuraci,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [2761] {accuraci,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [2762] {achiev,
## learn} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [2763] {accuraci,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [2764] {represent,
## accuraci} => {achiev} 0.1000000 0.7500000 3.214286 3
## [2765] {represent,
## achiev} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [2766] {accuraci,
## achiev} => {propos} 0.1000000 0.7500000 1.500000 3
## [2767] {accuraci,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [2768] {accuraci,
## achiev} => {featur} 0.1333333 1.0000000 1.875000 4
## [2769] {featur,
## accuraci} => {achiev} 0.1333333 0.8000000 3.428571 4
## [2770] {featur,
## achiev} => {accuraci} 0.1333333 0.8000000 4.000000 4
## [2771] {accuraci,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [2772] {network,
## accuraci} => {achiev} 0.1000000 0.7500000 3.214286 3
## [2773] {classif,
## accuraci} => {method} 0.1000000 1.0000000 2.727273 3
## [2774] {method,
## accuraci} => {classif} 0.1000000 1.0000000 3.750000 3
## [2775] {classif,
## accuraci} => {propos} 0.1000000 1.0000000 2.000000 3
## [2776] {accuraci,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [2777] {classif,
## accuraci} => {featur} 0.1000000 1.0000000 1.875000 3
## [2778] {paper,
## accuraci} => {train} 0.1000000 1.0000000 2.500000 3
## [2779] {train,
## accuraci} => {paper} 0.1000000 1.0000000 3.000000 3
## [2780] {paper,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [2781] {accuraci,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [2782] {paper,
## accuraci} => {represent} 0.1000000 1.0000000 2.000000 3
## [2783] {represent,
## accuraci} => {paper} 0.1000000 0.7500000 2.250000 3
## [2784] {paper,
## accuraci} => {featur} 0.1000000 1.0000000 1.875000 3
## [2785] {accuraci,
## recognit} => {perform} 0.1000000 0.7500000 1.607143 3
## [2786] {accuraci,
## perform} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2787] {perform,
## recognit} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [2788] {accuraci,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2789] {accuraci,
## dataset} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2790] {accuraci,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [2791] {accuraci,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [2792] {accuraci,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [2793] {represent,
## accuraci} => {recognit} 0.1000000 0.7500000 2.500000 3
## [2794] {accuraci,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [2795] {show,
## accuraci} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2796] {accuraci,
## recognit} => {propos} 0.1000000 0.7500000 1.500000 3
## [2797] {accuraci,
## propos} => {recognit} 0.1000000 0.7500000 2.500000 3
## [2798] {accuraci,
## recognit} => {featur} 0.1333333 1.0000000 1.875000 4
## [2799] {featur,
## accuraci} => {recognit} 0.1333333 0.8000000 2.666667 4
## [2800] {accuraci,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [2801] {accuraci,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [2802] {network,
## accuraci} => {result} 0.1000000 0.7500000 2.250000 3
## [2803] {accuraci,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [2804] {accuraci,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [2805] {accuraci,
## neural} => {featur} 0.1000000 1.0000000 1.875000 3
## [2806] {method,
## accuraci} => {propos} 0.1000000 1.0000000 2.000000 3
## [2807] {accuraci,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [2808] {method,
## accuraci} => {featur} 0.1000000 1.0000000 1.875000 3
## [2809] {train,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [2810] {accuraci,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [2811] {train,
## accuraci} => {represent} 0.1000000 1.0000000 2.000000 3
## [2812] {represent,
## accuraci} => {train} 0.1000000 0.7500000 1.875000 3
## [2813] {train,
## accuraci} => {featur} 0.1000000 1.0000000 1.875000 3
## [2814] {accuraci,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [2815] {accuraci,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [2816] {accuraci,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [2817] {accuraci,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [2818] {accuraci,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [2819] {represent,
## accuraci} => {learn} 0.1333333 1.0000000 2.307692 4
## [2820] {accuraci,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [2821] {accuraci,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [2822] {accuraci,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [2823] {featur,
## accuraci} => {learn} 0.1333333 0.8000000 1.846154 4
## [2824] {represent,
## accuraci} => {propos} 0.1000000 0.7500000 1.500000 3
## [2825] {accuraci,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [2826] {represent,
## accuraci} => {featur} 0.1333333 1.0000000 1.875000 4
## [2827] {featur,
## accuraci} => {represent} 0.1333333 0.8000000 1.600000 4
## [2828] {show,
## accuraci} => {featur} 0.1000000 1.0000000 1.875000 3
## [2829] {accuraci,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [2830] {featur,
## accuraci} => {propos} 0.1333333 0.8000000 1.600000 4
## [2831] {network,
## accuraci} => {featur} 0.1000000 0.7500000 1.406250 3
## [2832] {base,
## comput} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [2833] {algorithm,
## base} => {comput} 0.1000000 1.0000000 4.285714 3
## [2834] {algorithm,
## comput} => {base} 0.1000000 0.7500000 4.500000 3
## [2835] {neural,
## base} => {train} 0.1000000 0.7500000 1.875000 3
## [2836] {train,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [2837] {neural,
## base} => {approach} 0.1000000 0.7500000 1.875000 3
## [2838] {approach,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [2839] {neural,
## base} => {propos} 0.1000000 0.7500000 1.500000 3
## [2840] {propos,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [2841] {neural,
## base} => {network} 0.1333333 1.0000000 1.578947 4
## [2842] {network,
## base} => {neural} 0.1333333 1.0000000 3.000000 4
## [2843] {train,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [2844] {approach,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [2845] {train,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [2846] {propos,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [2847] {train,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [2848] {network,
## base} => {train} 0.1000000 0.7500000 1.875000 3
## [2849] {approach,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [2850] {propos,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [2851] {approach,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [2852] {network,
## base} => {approach} 0.1000000 0.7500000 1.875000 3
## [2853] {propos,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [2854] {network,
## base} => {propos} 0.1000000 0.7500000 1.500000 3
## [2855] {specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [2856] {result,
## potenti} => {specif} 0.1000000 0.7500000 4.500000 3
## [2857] {result,
## specif} => {potenti} 0.1000000 0.7500000 4.500000 3
## [2858] {specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [2859] {neural,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [2860] {neural,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [2861] {specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [2862] {train,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [2863] {specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2864] {dataset,
## potenti} => {specif} 0.1000000 0.7500000 4.500000 3
## [2865] {dataset,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [2866] {specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [2867] {network,
## potenti} => {specif} 0.1000000 0.7500000 4.500000 3
## [2868] {network,
## specif} => {potenti} 0.1000000 0.7500000 4.500000 3
## [2869] {layer,
## potenti} => {applic} 0.1000000 1.0000000 4.285714 3
## [2870] {applic,
## potenti} => {layer} 0.1000000 1.0000000 5.000000 3
## [2871] {layer,
## applic} => {potenti} 0.1000000 1.0000000 6.000000 3
## [2872] {layer,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [2873] {result,
## potenti} => {layer} 0.1000000 0.7500000 3.750000 3
## [2874] {layer,
## potenti} => {data} 0.1000000 1.0000000 2.307692 3
## [2875] {data,
## potenti} => {layer} 0.1000000 0.7500000 3.750000 3
## [2876] {data,
## layer} => {potenti} 0.1000000 0.7500000 4.500000 3
## [2877] {applic,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [2878] {result,
## potenti} => {applic} 0.1000000 0.7500000 3.214286 3
## [2879] {result,
## applic} => {potenti} 0.1000000 0.7500000 4.500000 3
## [2880] {applic,
## potenti} => {data} 0.1000000 1.0000000 2.307692 3
## [2881] {data,
## potenti} => {applic} 0.1000000 0.7500000 3.214286 3
## [2882] {result,
## potenti} => {neural} 0.1000000 0.7500000 2.250000 3
## [2883] {neural,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [2884] {result,
## potenti} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [2885] {algorithm,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [2886] {result,
## potenti} => {train} 0.1000000 0.7500000 1.875000 3
## [2887] {train,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [2888] {result,
## potenti} => {data} 0.1000000 0.7500000 1.730769 3
## [2889] {data,
## potenti} => {result} 0.1000000 0.7500000 2.250000 3
## [2890] {result,
## potenti} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2891] {dataset,
## potenti} => {result} 0.1000000 0.7500000 2.250000 3
## [2892] {result,
## potenti} => {network} 0.1000000 0.7500000 1.184211 3
## [2893] {network,
## potenti} => {result} 0.1000000 0.7500000 2.250000 3
## [2894] {neural,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [2895] {train,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [2896] {neural,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2897] {dataset,
## potenti} => {neural} 0.1000000 0.7500000 2.250000 3
## [2898] {neural,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [2899] {network,
## potenti} => {neural} 0.1000000 0.7500000 2.250000 3
## [2900] {train,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2901] {dataset,
## potenti} => {train} 0.1000000 0.7500000 1.875000 3
## [2902] {train,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [2903] {network,
## potenti} => {train} 0.1000000 0.7500000 1.875000 3
## [2904] {work,
## potenti} => {data} 0.1000000 1.0000000 2.307692 3
## [2905] {data,
## potenti} => {work} 0.1000000 0.7500000 1.875000 3
## [2906] {work,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2907] {dataset,
## potenti} => {work} 0.1000000 0.7500000 1.875000 3
## [2908] {work,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [2909] {network,
## potenti} => {work} 0.1000000 0.7500000 1.875000 3
## [2910] {data,
## potenti} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2911] {dataset,
## potenti} => {data} 0.1000000 0.7500000 1.730769 3
## [2912] {data,
## potenti} => {network} 0.1000000 0.7500000 1.184211 3
## [2913] {network,
## potenti} => {data} 0.1000000 0.7500000 1.730769 3
## [2914] {dataset,
## potenti} => {propos} 0.1000000 0.7500000 1.500000 3
## [2915] {propos,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2916] {dataset,
## potenti} => {network} 0.1333333 1.0000000 1.578947 4
## [2917] {network,
## potenti} => {dataset} 0.1333333 1.0000000 2.307692 4
## [2918] {propos,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [2919] {network,
## potenti} => {propos} 0.1000000 0.7500000 1.500000 3
## [2920] {solv,
## requir} => {problem} 0.1000000 1.0000000 3.333333 3
## [2921] {problem,
## solv} => {requir} 0.1000000 0.7500000 3.750000 3
## [2922] {problem,
## requir} => {solv} 0.1000000 1.0000000 5.000000 3
## [2923] {solv,
## requir} => {model} 0.1000000 1.0000000 1.875000 3
## [2924] {model,
## solv} => {requir} 0.1000000 1.0000000 5.000000 3
## [2925] {model,
## requir} => {solv} 0.1000000 0.7500000 3.750000 3
## [2926] {make,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [2927] {problem,
## solv} => {make} 0.1000000 0.7500000 2.500000 3
## [2928] {make,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [2929] {approach,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [2930] {make,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [2931] {perform,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [2932] {make,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2933] {featur,
## solv} => {make} 0.1000000 0.7500000 2.500000 3
## [2934] {problem,
## solv} => {approach} 0.1000000 0.7500000 1.875000 3
## [2935] {approach,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [2936] {problem,
## solv} => {perform} 0.1000000 0.7500000 1.607143 3
## [2937] {perform,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [2938] {problem,
## solv} => {model} 0.1000000 0.7500000 1.406250 3
## [2939] {model,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [2940] {problem,
## solv} => {featur} 0.1000000 0.7500000 1.406250 3
## [2941] {featur,
## solv} => {problem} 0.1000000 0.7500000 2.500000 3
## [2942] {task,
## solv} => {data} 0.1333333 1.0000000 2.307692 4
## [2943] {data,
## solv} => {task} 0.1333333 1.0000000 2.727273 4
## [2944] {task,
## solv} => {represent} 0.1000000 0.7500000 1.500000 3
## [2945] {represent,
## solv} => {task} 0.1000000 1.0000000 2.727273 3
## [2946] {task,
## solv} => {featur} 0.1000000 0.7500000 1.406250 3
## [2947] {featur,
## solv} => {task} 0.1000000 0.7500000 2.045455 3
## [2948] {approach,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [2949] {perform,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [2950] {approach,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2951] {featur,
## solv} => {approach} 0.1000000 0.7500000 1.875000 3
## [2952] {perform,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2953] {featur,
## solv} => {perform} 0.1000000 0.7500000 1.607143 3
## [2954] {data,
## solv} => {represent} 0.1000000 0.7500000 1.500000 3
## [2955] {represent,
## solv} => {data} 0.1000000 1.0000000 2.307692 3
## [2956] {data,
## solv} => {featur} 0.1000000 0.7500000 1.406250 3
## [2957] {featur,
## solv} => {data} 0.1000000 0.7500000 1.730769 3
## [2958] {dataset,
## solv} => {learn} 0.1000000 1.0000000 2.307692 3
## [2959] {learn,
## solv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2960] {dataset,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2961] {featur,
## solv} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2962] {learn,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2963] {featur,
## solv} => {learn} 0.1000000 0.7500000 1.730769 3
## [2964] {represent,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2965] {featur,
## solv} => {represent} 0.1000000 0.7500000 1.500000 3
## [2966] {layer,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [2967] {result,
## specif} => {layer} 0.1000000 0.7500000 3.750000 3
## [2968] {layer,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [2969] {train,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [2970] {layer,
## specif} => {work} 0.1000000 1.0000000 2.500000 3
## [2971] {work,
## specif} => {layer} 0.1000000 1.0000000 5.000000 3
## [2972] {work,
## layer} => {specif} 0.1000000 0.7500000 4.500000 3
## [2973] {layer,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [2974] {network,
## specif} => {layer} 0.1000000 0.7500000 3.750000 3
## [2975] {network,
## layer} => {specif} 0.1000000 0.7500000 4.500000 3
## [2976] {result,
## specif} => {neural} 0.1000000 0.7500000 2.250000 3
## [2977] {neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [2978] {result,
## specif} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [2979] {algorithm,
## specif} => {result} 0.1000000 0.7500000 2.250000 3
## [2980] {result,
## specif} => {train} 0.1333333 1.0000000 2.500000 4
## [2981] {train,
## specif} => {result} 0.1333333 0.8000000 2.400000 4
## [2982] {result,
## specif} => {work} 0.1000000 0.7500000 1.875000 3
## [2983] {work,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [2984] {result,
## work} => {specif} 0.1000000 0.7500000 4.500000 3
## [2985] {result,
## specif} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2986] {dataset,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [2987] {result,
## specif} => {network} 0.1333333 1.0000000 1.578947 4
## [2988] {network,
## specif} => {result} 0.1333333 1.0000000 3.000000 4
## [2989] {neural,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [2990] {neural,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2991] {dataset,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [2992] {neural,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [2993] {network,
## specif} => {neural} 0.1000000 0.7500000 2.250000 3
## [2994] {algorithm,
## specif} => {train} 0.1333333 1.0000000 2.500000 4
## [2995] {train,
## specif} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [2996] {algorithm,
## specif} => {network} 0.1000000 0.7500000 1.184211 3
## [2997] {network,
## specif} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [2998] {work,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [2999] {data,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [3000] {dataset,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [3001] {propos,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [3002] {train,
## specif} => {network} 0.1333333 0.8000000 1.263158 4
## [3003] {network,
## specif} => {train} 0.1333333 1.0000000 2.500000 4
## [3004] {work,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [3005] {network,
## specif} => {work} 0.1000000 0.7500000 1.875000 3
## [3006] {dataset,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [3007] {network,
## specif} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3008] {layer,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3009] {result,
## techniqu} => {layer} 0.1000000 0.7500000 3.750000 3
## [3010] {layer,
## techniqu} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3011] {algorithm,
## techniqu} => {layer} 0.1000000 1.0000000 5.000000 3
## [3012] {machin,
## techniqu} => {learn} 0.1000000 1.0000000 2.307692 3
## [3013] {learn,
## techniqu} => {machin} 0.1000000 1.0000000 4.285714 3
## [3014] {machin,
## learn} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [3015] {machin,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [3016] {featur,
## techniqu} => {machin} 0.1000000 0.7500000 3.214286 3
## [3017] {success,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3018] {result,
## techniqu} => {success} 0.1000000 0.7500000 2.812500 3
## [3019] {result,
## success} => {techniqu} 0.1000000 1.0000000 6.000000 3
## [3020] {architectur,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3021] {result,
## techniqu} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3022] {architectur,
## result} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [3023] {architectur,
## techniqu} => {represent} 0.1000000 1.0000000 2.000000 3
## [3024] {represent,
## techniqu} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3025] {represent,
## architectur} => {techniqu} 0.1000000 1.0000000 6.000000 3
## [3026] {architectur,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [3027] {featur,
## techniqu} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3028] {result,
## techniqu} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3029] {algorithm,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3030] {result,
## techniqu} => {train} 0.1000000 0.7500000 1.875000 3
## [3031] {train,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3032] {result,
## techniqu} => {represent} 0.1000000 0.7500000 1.500000 3
## [3033] {represent,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3034] {result,
## techniqu} => {featur} 0.1000000 0.7500000 1.406250 3
## [3035] {featur,
## techniqu} => {result} 0.1000000 0.7500000 2.250000 3
## [3036] {result,
## techniqu} => {network} 0.1000000 0.7500000 1.184211 3
## [3037] {network,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3038] {train,
## techniqu} => {network} 0.1000000 1.0000000 1.578947 3
## [3039] {network,
## techniqu} => {train} 0.1000000 1.0000000 2.500000 3
## [3040] {learn,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [3041] {featur,
## techniqu} => {learn} 0.1000000 0.7500000 1.730769 3
## [3042] {represent,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [3043] {featur,
## techniqu} => {represent} 0.1000000 0.7500000 1.500000 3
## [3044] {appli,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [3045] {represent,
## appli} => {applic} 0.1000000 1.0000000 4.285714 3
## [3046] {appli,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [3047] {appli,
## object} => {perform} 0.1000000 1.0000000 2.142857 3
## [3048] {appli,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [3049] {appli,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [3050] {featur,
## appli} => {object} 0.1000000 0.7500000 2.812500 3
## [3051] {appli,
## architectur} => {method} 0.1000000 1.0000000 2.727273 3
## [3052] {method,
## appli} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3053] {method,
## architectur} => {appli} 0.1000000 0.7500000 3.750000 3
## [3054] {appli,
## architectur} => {perform} 0.1000000 1.0000000 2.142857 3
## [3055] {appli,
## architectur} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3056] {appli,
## dataset} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3057] {appli,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [3058] {appli,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [3059] {network,
## appli} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3060] {classif,
## appli} => {method} 0.1000000 1.0000000 2.727273 3
## [3061] {method,
## appli} => {classif} 0.1000000 0.7500000 2.812500 3
## [3062] {classif,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [3063] {classif,
## appli} => {show} 0.1000000 1.0000000 1.875000 3
## [3064] {show,
## appli} => {classif} 0.1000000 0.7500000 2.812500 3
## [3065] {classif,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [3066] {appli,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [3067] {appli,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [3068] {appli,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [3069] {show,
## appli} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3070] {appli,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [3071] {appli,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [3072] {approach,
## appli} => {neural} 0.1000000 0.7500000 2.250000 3
## [3073] {appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3074] {appli,
## dataset} => {neural} 0.1000000 0.7500000 2.250000 3
## [3075] {appli,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [3076] {show,
## appli} => {neural} 0.1000000 0.7500000 2.250000 3
## [3077] {appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [3078] {appli,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [3079] {network,
## appli} => {neural} 0.1000000 0.7500000 2.250000 3
## [3080] {method,
## appli} => {perform} 0.1333333 1.0000000 2.142857 4
## [3081] {appli,
## perform} => {method} 0.1333333 0.8000000 2.181818 4
## [3082] {method,
## appli} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3083] {appli,
## dataset} => {method} 0.1000000 0.7500000 2.045455 3
## [3084] {method,
## appli} => {show} 0.1000000 0.7500000 1.406250 3
## [3085] {show,
## appli} => {method} 0.1000000 0.7500000 2.045455 3
## [3086] {method,
## appli} => {propos} 0.1333333 1.0000000 2.000000 4
## [3087] {method,
## appli} => {featur} 0.1000000 0.7500000 1.406250 3
## [3088] {featur,
## appli} => {method} 0.1000000 0.7500000 2.045455 3
## [3089] {method,
## appli} => {network} 0.1000000 0.7500000 1.184211 3
## [3090] {network,
## appli} => {method} 0.1000000 0.7500000 2.045455 3
## [3091] {algorithm,
## appli} => {approach} 0.1000000 1.0000000 2.500000 3
## [3092] {approach,
## appli} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3093] {algorithm,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [3094] {algorithm,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [3095] {approach,
## appli} => {perform} 0.1000000 0.7500000 1.607143 3
## [3096] {approach,
## appli} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3097] {appli,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [3098] {approach,
## appli} => {show} 0.1000000 0.7500000 1.406250 3
## [3099] {show,
## appli} => {approach} 0.1000000 0.7500000 1.875000 3
## [3100] {approach,
## appli} => {propos} 0.1333333 1.0000000 2.000000 4
## [3101] {approach,
## appli} => {network} 0.1000000 0.7500000 1.184211 3
## [3102] {network,
## appli} => {approach} 0.1000000 0.7500000 1.875000 3
## [3103] {appli,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3104] {appli,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [3105] {appli,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [3106] {appli,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [3107] {network,
## appli} => {work} 0.1000000 0.7500000 1.875000 3
## [3108] {appli,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [3109] {show,
## appli} => {perform} 0.1000000 0.7500000 1.607143 3
## [3110] {appli,
## perform} => {propos} 0.1666667 1.0000000 2.000000 5
## [3111] {appli,
## propos} => {perform} 0.1666667 0.8333333 1.785714 5
## [3112] {appli,
## perform} => {featur} 0.1333333 0.8000000 1.500000 4
## [3113] {featur,
## appli} => {perform} 0.1333333 1.0000000 2.142857 4
## [3114] {network,
## appli} => {perform} 0.1000000 0.7500000 1.607143 3
## [3115] {appli,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [3116] {show,
## appli} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3117] {appli,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [3118] {appli,
## dataset} => {network} 0.1333333 1.0000000 1.578947 4
## [3119] {network,
## appli} => {dataset} 0.1333333 1.0000000 2.307692 4
## [3120] {represent,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [3121] {show,
## appli} => {propos} 0.1333333 1.0000000 2.000000 4
## [3122] {show,
## appli} => {network} 0.1000000 0.7500000 1.184211 3
## [3123] {network,
## appli} => {show} 0.1000000 0.7500000 1.406250 3
## [3124] {featur,
## appli} => {propos} 0.1333333 1.0000000 2.000000 4
## [3125] {network,
## appli} => {propos} 0.1333333 1.0000000 2.000000 4
## [3126] {perform,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3127] {data,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3128] {dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3129] {work,
## larg} => {represent} 0.1333333 0.8000000 1.600000 4
## [3130] {represent,
## larg} => {work} 0.1333333 1.0000000 2.500000 4
## [3131] {propos,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3132] {featur,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3133] {network,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3134] {perform,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [3135] {represent,
## larg} => {perform} 0.1000000 0.7500000 1.607143 3
## [3136] {data,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3137] {dataset,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [3138] {data,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [3139] {represent,
## larg} => {data} 0.1000000 0.7500000 1.730769 3
## [3140] {data,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [3141] {featur,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [3142] {dataset,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [3143] {represent,
## larg} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3144] {dataset,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [3145] {featur,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3146] {represent,
## larg} => {propos} 0.1000000 0.7500000 1.500000 3
## [3147] {propos,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [3148] {represent,
## larg} => {featur} 0.1000000 0.7500000 1.406250 3
## [3149] {featur,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [3150] {achiev,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [3151] {data,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [3152] {data,
## achiev} => {challeng} 0.1000000 0.7500000 4.500000 3
## [3153] {achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3154] {dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [3155] {achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [3156] {learn,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [3157] {achiev,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [3158] {achiev,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [3159] {represent,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [3160] {represent,
## achiev} => {challeng} 0.1000000 0.7500000 4.500000 3
## [3161] {achiev,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [3162] {featur,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [3163] {signific,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [3164] {train,
## challeng} => {signific} 0.1000000 0.7500000 2.812500 3
## [3165] {signific,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [3166] {show,
## challeng} => {signific} 0.1000000 0.7500000 2.812500 3
## [3167] {object,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [3168] {show,
## challeng} => {object} 0.1000000 0.7500000 2.812500 3
## [3169] {object,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [3170] {propos,
## challeng} => {object} 0.1000000 0.7500000 2.812500 3
## [3171] {paper,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [3172] {train,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [3173] {paper,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [3174] {data,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [3175] {paper,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [3176] {learn,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [3177] {paper,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [3178] {featur,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [3179] {recognit,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [3180] {train,
## challeng} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3181] {train,
## recognit} => {challeng} 0.1000000 0.7500000 4.500000 3
## [3182] {recognit,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [3183] {represent,
## challeng} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3184] {task,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [3185] {data,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [3186] {task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [3187] {learn,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [3188] {task,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [3189] {show,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [3190] {task,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [3191] {featur,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [3192] {train,
## challeng} => {perform} 0.1000000 0.7500000 1.607143 3
## [3193] {perform,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [3194] {train,
## challeng} => {data} 0.1000000 0.7500000 1.730769 3
## [3195] {data,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3196] {train,
## challeng} => {learn} 0.1000000 0.7500000 1.730769 3
## [3197] {learn,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3198] {train,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3199] {represent,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3200] {train,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3201] {show,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3202] {train,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3203] {propos,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3204] {train,
## challeng} => {featur} 0.1000000 0.7500000 1.406250 3
## [3205] {featur,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3206] {perform,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [3207] {propos,
## challeng} => {perform} 0.1000000 0.7500000 1.607143 3
## [3208] {data,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3209] {dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [3210] {data,
## challeng} => {learn} 0.1333333 1.0000000 2.307692 4
## [3211] {learn,
## challeng} => {data} 0.1333333 1.0000000 2.307692 4
## [3212] {data,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3213] {represent,
## challeng} => {data} 0.1000000 0.7500000 1.730769 3
## [3214] {data,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3215] {show,
## challeng} => {data} 0.1000000 0.7500000 1.730769 3
## [3216] {data,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3217] {propos,
## challeng} => {data} 0.1000000 0.7500000 1.730769 3
## [3218] {data,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [3219] {model,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [3220] {data,
## challeng} => {featur} 0.1333333 1.0000000 1.875000 4
## [3221] {featur,
## challeng} => {data} 0.1333333 1.0000000 2.307692 4
## [3222] {dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [3223] {learn,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3224] {dataset,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [3225] {represent,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3226] {dataset,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [3227] {featur,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3228] {learn,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3229] {represent,
## challeng} => {learn} 0.1000000 0.7500000 1.730769 3
## [3230] {learn,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3231] {show,
## challeng} => {learn} 0.1000000 0.7500000 1.730769 3
## [3232] {learn,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3233] {propos,
## challeng} => {learn} 0.1000000 0.7500000 1.730769 3
## [3234] {learn,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [3235] {model,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [3236] {learn,
## challeng} => {featur} 0.1333333 1.0000000 1.875000 4
## [3237] {featur,
## challeng} => {learn} 0.1333333 1.0000000 2.307692 4
## [3238] {represent,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3239] {show,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3240] {represent,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3241] {propos,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3242] {represent,
## challeng} => {featur} 0.1000000 0.7500000 1.406250 3
## [3243] {featur,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3244] {show,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3245] {propos,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3246] {show,
## challeng} => {featur} 0.1000000 0.7500000 1.406250 3
## [3247] {featur,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3248] {propos,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [3249] {model,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [3250] {propos,
## challeng} => {featur} 0.1000000 0.7500000 1.406250 3
## [3251] {featur,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3252] {model,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [3253] {featur,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [3254] {demonstr,
## imag} => {work} 0.1000000 1.0000000 2.500000 3
## [3255] {imag,
## work} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [3256] {demonstr,
## work} => {imag} 0.1000000 0.7500000 4.500000 3
## [3257] {demonstr,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3258] {propos,
## demonstr} => {imag} 0.1000000 0.7500000 4.500000 3
## [3259] {object,
## imag} => {recognit} 0.1000000 1.0000000 3.333333 3
## [3260] {recognit,
## imag} => {object} 0.1000000 0.7500000 2.812500 3
## [3261] {object,
## recognit} => {imag} 0.1000000 0.7500000 4.500000 3
## [3262] {object,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3263] {classif,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3264] {recognit,
## imag} => {perform} 0.1000000 0.7500000 1.607143 3
## [3265] {perform,
## imag} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3266] {perform,
## recognit} => {imag} 0.1000000 0.7500000 4.500000 3
## [3267] {recognit,
## imag} => {learn} 0.1000000 0.7500000 1.730769 3
## [3268] {imag,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [3269] {recognit,
## imag} => {represent} 0.1000000 0.7500000 1.500000 3
## [3270] {represent,
## imag} => {recognit} 0.1000000 1.0000000 3.333333 3
## [3271] {recognit,
## imag} => {propos} 0.1333333 1.0000000 2.000000 4
## [3272] {propos,
## imag} => {recognit} 0.1333333 0.8000000 2.666667 4
## [3273] {recognit,
## imag} => {featur} 0.1000000 0.7500000 1.406250 3
## [3274] {featur,
## imag} => {recognit} 0.1000000 1.0000000 3.333333 3
## [3275] {improv,
## imag} => {train} 0.1000000 1.0000000 2.500000 3
## [3276] {train,
## imag} => {improv} 0.1000000 1.0000000 3.333333 3
## [3277] {improv,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [3278] {perform,
## imag} => {improv} 0.1000000 0.7500000 2.500000 3
## [3279] {improv,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3280] {train,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [3281] {perform,
## imag} => {train} 0.1000000 0.7500000 1.875000 3
## [3282] {train,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3283] {imag,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [3284] {perform,
## imag} => {represent} 0.1000000 0.7500000 1.500000 3
## [3285] {represent,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [3286] {perform,
## imag} => {show} 0.1000000 0.7500000 1.406250 3
## [3287] {show,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [3288] {perform,
## imag} => {propos} 0.1333333 1.0000000 2.000000 4
## [3289] {propos,
## imag} => {perform} 0.1333333 0.8000000 1.714286 4
## [3290] {dataset,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3291] {imag,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [3292] {imag,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [3293] {featur,
## imag} => {learn} 0.1000000 1.0000000 2.307692 3
## [3294] {represent,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3295] {show,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3296] {featur,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3297] {framework,
## requir} => {train} 0.1000000 1.0000000 2.500000 3
## [3298] {train,
## framework} => {requir} 0.1000000 0.7500000 3.750000 3
## [3299] {train,
## requir} => {framework} 0.1000000 0.7500000 3.750000 3
## [3300] {comput,
## framework} => {reduc} 0.1000000 1.0000000 4.285714 3
## [3301] {reduc,
## framework} => {comput} 0.1000000 1.0000000 4.285714 3
## [3302] {reduc,
## comput} => {framework} 0.1000000 0.7500000 3.750000 3
## [3303] {train,
## framework} => {work} 0.1000000 0.7500000 1.875000 3
## [3304] {work,
## framework} => {train} 0.1000000 0.7500000 1.875000 3
## [3305] {train,
## framework} => {show} 0.1000000 0.7500000 1.406250 3
## [3306] {show,
## framework} => {train} 0.1000000 1.0000000 2.500000 3
## [3307] {work,
## framework} => {network} 0.1000000 0.7500000 1.184211 3
## [3308] {network,
## framework} => {work} 0.1000000 0.7500000 1.875000 3
## [3309] {demonstr,
## requir} => {perform} 0.1000000 1.0000000 2.142857 3
## [3310] {perform,
## requir} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [3311] {perform,
## demonstr} => {requir} 0.1000000 0.7500000 3.750000 3
## [3312] {problem,
## requir} => {model} 0.1000000 1.0000000 1.875000 3
## [3313] {model,
## requir} => {problem} 0.1000000 0.7500000 2.500000 3
## [3314] {improv,
## requir} => {train} 0.1000000 0.7500000 1.875000 3
## [3315] {train,
## requir} => {improv} 0.1000000 0.7500000 2.500000 3
## [3316] {improv,
## requir} => {show} 0.1000000 0.7500000 1.406250 3
## [3317] {show,
## requir} => {improv} 0.1000000 1.0000000 3.333333 3
## [3318] {improv,
## requir} => {model} 0.1000000 0.7500000 1.406250 3
## [3319] {model,
## requir} => {improv} 0.1000000 0.7500000 2.500000 3
## [3320] {algorithm,
## requir} => {train} 0.1000000 1.0000000 2.500000 3
## [3321] {train,
## requir} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3322] {approach,
## requir} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3323] {dataset,
## requir} => {approach} 0.1000000 1.0000000 2.500000 3
## [3324] {approach,
## requir} => {model} 0.1000000 1.0000000 1.875000 3
## [3325] {model,
## requir} => {approach} 0.1000000 0.7500000 1.875000 3
## [3326] {dataset,
## requir} => {model} 0.1000000 1.0000000 1.875000 3
## [3327] {model,
## requir} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3328] {reduc,
## exist} => {optim} 0.1000000 1.0000000 4.285714 3
## [3329] {exist,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [3330] {reduc,
## optim} => {exist} 0.1000000 0.7500000 4.500000 3
## [3331] {reduc,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [3332] {exist,
## work} => {reduc} 0.1000000 0.7500000 3.214286 3
## [3333] {reduc,
## work} => {exist} 0.1000000 1.0000000 6.000000 3
## [3334] {reduc,
## exist} => {network} 0.1000000 1.0000000 1.578947 3
## [3335] {network,
## exist} => {reduc} 0.1000000 0.7500000 3.214286 3
## [3336] {exist,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [3337] {exist,
## work} => {optim} 0.1000000 0.7500000 3.214286 3
## [3338] {optim,
## work} => {exist} 0.1000000 0.7500000 4.500000 3
## [3339] {exist,
## optim} => {network} 0.1000000 1.0000000 1.578947 3
## [3340] {network,
## exist} => {optim} 0.1000000 0.7500000 3.214286 3
## [3341] {network,
## optim} => {exist} 0.1000000 0.7500000 4.500000 3
## [3342] {improv,
## exist} => {perform} 0.1000000 1.0000000 2.142857 3
## [3343] {perform,
## exist} => {improv} 0.1000000 1.0000000 3.333333 3
## [3344] {approach,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [3345] {exist,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [3346] {approach,
## exist} => {show} 0.1000000 1.0000000 1.875000 3
## [3347] {show,
## exist} => {approach} 0.1000000 1.0000000 2.500000 3
## [3348] {approach,
## exist} => {network} 0.1000000 1.0000000 1.578947 3
## [3349] {network,
## exist} => {approach} 0.1000000 0.7500000 1.875000 3
## [3350] {exist,
## work} => {show} 0.1000000 0.7500000 1.406250 3
## [3351] {show,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [3352] {exist,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [3353] {model,
## exist} => {work} 0.1000000 0.7500000 1.875000 3
## [3354] {exist,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [3355] {network,
## exist} => {work} 0.1333333 1.0000000 2.500000 4
## [3356] {dataset,
## exist} => {propos} 0.1000000 1.0000000 2.000000 3
## [3357] {propos,
## exist} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3358] {exist,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [3359] {represent,
## exist} => {learn} 0.1000000 1.0000000 2.307692 3
## [3360] {exist,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [3361] {model,
## exist} => {learn} 0.1000000 0.7500000 1.730769 3
## [3362] {exist,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [3363] {featur,
## exist} => {learn} 0.1000000 1.0000000 2.307692 3
## [3364] {represent,
## exist} => {model} 0.1000000 1.0000000 1.875000 3
## [3365] {model,
## exist} => {represent} 0.1000000 0.7500000 1.500000 3
## [3366] {represent,
## exist} => {featur} 0.1000000 1.0000000 1.875000 3
## [3367] {featur,
## exist} => {represent} 0.1000000 1.0000000 2.000000 3
## [3368] {show,
## exist} => {network} 0.1000000 1.0000000 1.578947 3
## [3369] {network,
## exist} => {show} 0.1000000 0.7500000 1.406250 3
## [3370] {model,
## exist} => {featur} 0.1000000 0.7500000 1.406250 3
## [3371] {featur,
## exist} => {model} 0.1000000 1.0000000 1.875000 3
## [3372] {model,
## exist} => {network} 0.1000000 0.7500000 1.184211 3
## [3373] {network,
## exist} => {model} 0.1000000 0.7500000 1.406250 3
## [3374] {layer,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [3375] {result,
## applic} => {layer} 0.1000000 0.7500000 3.750000 3
## [3376] {layer,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [3377] {data,
## layer} => {applic} 0.1000000 0.7500000 3.214286 3
## [3378] {process,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3379] {process,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [3380] {represent,
## layer} => {process} 0.1000000 0.7500000 3.750000 3
## [3381] {represent,
## process} => {layer} 0.1000000 1.0000000 5.000000 3
## [3382] {process,
## layer} => {featur} 0.1000000 1.0000000 1.875000 3
## [3383] {featur,
## layer} => {process} 0.1000000 1.0000000 5.000000 3
## [3384] {result,
## layer} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [3385] {algorithm,
## layer} => {result} 0.1333333 0.8000000 2.400000 4
## [3386] {train,
## layer} => {result} 0.1000000 1.0000000 3.000000 3
## [3387] {work,
## layer} => {result} 0.1000000 0.7500000 2.250000 3
## [3388] {result,
## work} => {layer} 0.1000000 0.7500000 3.750000 3
## [3389] {result,
## layer} => {data} 0.1333333 0.8000000 1.846154 4
## [3390] {data,
## layer} => {result} 0.1333333 1.0000000 3.000000 4
## [3391] {represent,
## layer} => {result} 0.1000000 0.7500000 2.250000 3
## [3392] {show,
## layer} => {result} 0.1000000 0.7500000 2.250000 3
## [3393] {network,
## layer} => {result} 0.1000000 0.7500000 2.250000 3
## [3394] {neural,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [3395] {work,
## layer} => {neural} 0.1000000 0.7500000 2.250000 3
## [3396] {neural,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [3397] {represent,
## layer} => {neural} 0.1000000 0.7500000 2.250000 3
## [3398] {neural,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [3399] {network,
## layer} => {neural} 0.1000000 0.7500000 2.250000 3
## [3400] {work,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3401] {algorithm,
## work} => {layer} 0.1000000 0.7500000 3.750000 3
## [3402] {data,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3403] {represent,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3404] {represent,
## algorithm} => {layer} 0.1000000 0.7500000 3.750000 3
## [3405] {show,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3406] {model,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3407] {featur,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3408] {network,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3409] {train,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [3410] {work,
## layer} => {train} 0.1000000 0.7500000 1.875000 3
## [3411] {train,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [3412] {network,
## layer} => {train} 0.1000000 0.7500000 1.875000 3
## [3413] {work,
## layer} => {represent} 0.1000000 0.7500000 1.500000 3
## [3414] {represent,
## layer} => {work} 0.1000000 0.7500000 1.875000 3
## [3415] {work,
## layer} => {network} 0.1333333 1.0000000 1.578947 4
## [3416] {network,
## layer} => {work} 0.1333333 1.0000000 2.500000 4
## [3417] {data,
## layer} => {represent} 0.1000000 0.7500000 1.500000 3
## [3418] {represent,
## layer} => {data} 0.1000000 0.7500000 1.730769 3
## [3419] {data,
## layer} => {show} 0.1000000 0.7500000 1.406250 3
## [3420] {show,
## layer} => {data} 0.1000000 0.7500000 1.730769 3
## [3421] {layer,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [3422] {represent,
## layer} => {learn} 0.1000000 0.7500000 1.730769 3
## [3423] {layer,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [3424] {show,
## layer} => {learn} 0.1000000 0.7500000 1.730769 3
## [3425] {represent,
## layer} => {show} 0.1000000 0.7500000 1.406250 3
## [3426] {show,
## layer} => {represent} 0.1000000 0.7500000 1.500000 3
## [3427] {represent,
## layer} => {featur} 0.1000000 0.7500000 1.406250 3
## [3428] {featur,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [3429] {represent,
## layer} => {network} 0.1000000 0.7500000 1.184211 3
## [3430] {network,
## layer} => {represent} 0.1000000 0.7500000 1.500000 3
## [3431] {show,
## layer} => {model} 0.1000000 0.7500000 1.406250 3
## [3432] {model,
## layer} => {show} 0.1000000 1.0000000 1.875000 3
## [3433] {complex,
## input} => {approach} 0.1000000 1.0000000 2.500000 3
## [3434] {approach,
## complex} => {input} 0.1000000 1.0000000 4.285714 3
## [3435] {complex,
## input} => {show} 0.1000000 1.0000000 1.875000 3
## [3436] {complex,
## input} => {model} 0.1000000 1.0000000 1.875000 3
## [3437] {complex,
## model} => {input} 0.1000000 1.0000000 4.285714 3
## [3438] {complex,
## input} => {featur} 0.1000000 1.0000000 1.875000 3
## [3439] {featur,
## input} => {complex} 0.1000000 0.7500000 3.750000 3
## [3440] {classif,
## complex} => {method} 0.1000000 1.0000000 2.727273 3
## [3441] {complex,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [3442] {classif,
## complex} => {show} 0.1000000 1.0000000 1.875000 3
## [3443] {classif,
## complex} => {featur} 0.1000000 1.0000000 1.875000 3
## [3444] {complex,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [3445] {complex,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [3446] {complex,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [3447] {complex,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [3448] {complex,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [3449] {approach,
## complex} => {show} 0.1000000 1.0000000 1.875000 3
## [3450] {approach,
## complex} => {model} 0.1000000 1.0000000 1.875000 3
## [3451] {complex,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [3452] {approach,
## complex} => {featur} 0.1000000 1.0000000 1.875000 3
## [3453] {complex,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [3454] {complex,
## perform} => {represent} 0.1000000 0.7500000 1.500000 3
## [3455] {complex,
## perform} => {show} 0.1000000 0.7500000 1.406250 3
## [3456] {complex,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [3457] {complex,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [3458] {complex,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [3459] {complex,
## represent} => {show} 0.1333333 0.8000000 1.500000 4
## [3460] {complex,
## show} => {represent} 0.1333333 0.8000000 1.600000 4
## [3461] {complex,
## represent} => {featur} 0.1333333 0.8000000 1.500000 4
## [3462] {complex,
## featur} => {represent} 0.1333333 0.8000000 1.600000 4
## [3463] {complex,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [3464] {complex,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [3465] {complex,
## show} => {featur} 0.1333333 0.8000000 1.500000 4
## [3466] {complex,
## featur} => {show} 0.1333333 0.8000000 1.500000 4
## [3467] {complex,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [3468] {complex,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [3469] {achiev,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [3470] {general,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [3471] {achiev,
## result} => {general} 0.1000000 0.7500000 3.750000 3
## [3472] {achiev,
## general} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3473] {dataset,
## general} => {achiev} 0.1000000 0.7500000 3.214286 3
## [3474] {achiev,
## general} => {network} 0.1000000 1.0000000 1.578947 3
## [3475] {network,
## general} => {achiev} 0.1000000 1.0000000 4.285714 3
## [3476] {general,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [3477] {represent,
## general} => {signific} 0.1000000 0.7500000 2.812500 3
## [3478] {general,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [3479] {general,
## recognit} => {result} 0.1000000 0.7500000 2.250000 3
## [3480] {general,
## result} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3481] {recognit,
## result} => {general} 0.1000000 0.7500000 3.750000 3
## [3482] {general,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [3483] {represent,
## general} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3484] {general,
## recognit} => {show} 0.1333333 1.0000000 1.875000 4
## [3485] {show,
## general} => {recognit} 0.1333333 0.8000000 2.666667 4
## [3486] {general,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [3487] {featur,
## general} => {recognit} 0.1000000 1.0000000 3.333333 3
## [3488] {general,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [3489] {general,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [3490] {general,
## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3491] {algorithm,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [3492] {general,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3493] {dataset,
## general} => {result} 0.1000000 0.7500000 2.250000 3
## [3494] {general,
## result} => {show} 0.1000000 0.7500000 1.406250 3
## [3495] {general,
## result} => {model} 0.1000000 0.7500000 1.406250 3
## [3496] {model,
## general} => {result} 0.1000000 0.7500000 2.250000 3
## [3497] {general,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [3498] {featur,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [3499] {general,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [3500] {network,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [3501] {algorithm,
## general} => {model} 0.1000000 1.0000000 1.875000 3
## [3502] {model,
## general} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3503] {approach,
## general} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3504] {dataset,
## general} => {approach} 0.1000000 0.7500000 1.875000 3
## [3505] {approach,
## general} => {propos} 0.1000000 1.0000000 2.000000 3
## [3506] {general,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [3507] {approach,
## general} => {model} 0.1000000 1.0000000 1.875000 3
## [3508] {model,
## general} => {approach} 0.1000000 0.7500000 1.875000 3
## [3509] {general,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [3510] {general,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [3511] {general,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [3512] {dataset,
## general} => {show} 0.1000000 0.7500000 1.406250 3
## [3513] {dataset,
## general} => {propos} 0.1000000 0.7500000 1.500000 3
## [3514] {general,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3515] {dataset,
## general} => {model} 0.1000000 0.7500000 1.406250 3
## [3516] {model,
## general} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3517] {dataset,
## general} => {network} 0.1000000 0.7500000 1.184211 3
## [3518] {network,
## general} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3519] {general,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [3520] {represent,
## general} => {learn} 0.1000000 0.7500000 1.730769 3
## [3521] {general,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [3522] {represent,
## general} => {show} 0.1333333 1.0000000 1.875000 4
## [3523] {show,
## general} => {represent} 0.1333333 0.8000000 1.600000 4
## [3524] {general,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [3525] {model,
## general} => {show} 0.1000000 0.7500000 1.406250 3
## [3526] {featur,
## general} => {show} 0.1000000 1.0000000 1.875000 3
## [3527] {general,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [3528] {model,
## general} => {propos} 0.1000000 0.7500000 1.500000 3
## [3529] {signific,
## effect} => {paper} 0.1333333 1.0000000 3.000000 4
## [3530] {paper,
## effect} => {signific} 0.1333333 0.8000000 3.000000 4
## [3531] {paper,
## signific} => {effect} 0.1333333 1.0000000 4.285714 4
## [3532] {signific,
## effect} => {task} 0.1000000 0.7500000 2.045455 3
## [3533] {task,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [3534] {task,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [3535] {signific,
## effect} => {train} 0.1000000 0.7500000 1.875000 3
## [3536] {train,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [3537] {signific,
## effect} => {learn} 0.1333333 1.0000000 2.307692 4
## [3538] {learn,
## effect} => {signific} 0.1333333 0.8000000 3.000000 4
## [3539] {learn,
## signific} => {effect} 0.1333333 1.0000000 4.285714 4
## [3540] {signific,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [3541] {signific,
## effect} => {show} 0.1000000 0.7500000 1.406250 3
## [3542] {signific,
## effect} => {propos} 0.1000000 0.7500000 1.500000 3
## [3543] {propos,
## effect} => {signific} 0.1000000 0.7500000 2.812500 3
## [3544] {signific,
## effect} => {featur} 0.1000000 0.7500000 1.406250 3
## [3545] {featur,
## effect} => {signific} 0.1000000 0.7500000 2.812500 3
## [3546] {effect,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [3547] {method,
## effect} => {success} 0.1000000 1.0000000 3.750000 3
## [3548] {method,
## success} => {effect} 0.1000000 0.7500000 3.214286 3
## [3549] {effect,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [3550] {effect,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [3551] {effect,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [3552] {propos,
## effect} => {success} 0.1000000 0.7500000 2.812500 3
## [3553] {classif,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [3554] {classif,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [3555] {propos,
## effect} => {classif} 0.1000000 0.7500000 2.812500 3
## [3556] {classif,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [3557] {featur,
## effect} => {classif} 0.1000000 0.7500000 2.812500 3
## [3558] {problem,
## effect} => {perform} 0.1000000 1.0000000 2.142857 3
## [3559] {perform,
## effect} => {problem} 0.1000000 1.0000000 3.333333 3
## [3560] {problem,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [3561] {problem,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [3562] {problem,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [3563] {propos,
## effect} => {problem} 0.1000000 0.7500000 2.500000 3
## [3564] {task,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [3565] {train,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [3566] {paper,
## effect} => {learn} 0.1333333 0.8000000 1.846154 4
## [3567] {learn,
## effect} => {paper} 0.1333333 0.8000000 2.400000 4
## [3568] {paper,
## learn} => {effect} 0.1333333 0.8000000 3.428571 4
## [3569] {paper,
## effect} => {represent} 0.1333333 0.8000000 1.600000 4
## [3570] {represent,
## effect} => {paper} 0.1333333 0.8000000 2.400000 4
## [3571] {paper,
## effect} => {show} 0.1333333 0.8000000 1.500000 4
## [3572] {show,
## effect} => {paper} 0.1333333 0.8000000 2.400000 4
## [3573] {propos,
## effect} => {paper} 0.1000000 0.7500000 2.250000 3
## [3574] {paper,
## propos} => {effect} 0.1000000 0.7500000 3.214286 3
## [3575] {model,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [3576] {featur,
## effect} => {paper} 0.1000000 0.7500000 2.250000 3
## [3577] {network,
## effect} => {paper} 0.1000000 0.7500000 2.250000 3
## [3578] {neural,
## effect} => {network} 0.1000000 1.0000000 1.578947 3
## [3579] {network,
## effect} => {neural} 0.1000000 0.7500000 2.250000 3
## [3580] {method,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [3581] {method,
## effect} => {represent} 0.1000000 1.0000000 2.000000 3
## [3582] {method,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [3583] {propos,
## effect} => {method} 0.1000000 0.7500000 2.045455 3
## [3584] {task,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [3585] {train,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [3586] {task,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [3587] {task,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [3588] {featur,
## effect} => {task} 0.1000000 0.7500000 2.045455 3
## [3589] {train,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [3590] {train,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [3591] {featur,
## effect} => {train} 0.1000000 0.7500000 1.875000 3
## [3592] {perform,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [3593] {perform,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [3594] {perform,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [3595] {propos,
## effect} => {perform} 0.1000000 0.7500000 1.607143 3
## [3596] {learn,
## effect} => {represent} 0.1333333 0.8000000 1.600000 4
## [3597] {represent,
## effect} => {learn} 0.1333333 0.8000000 1.846154 4
## [3598] {learn,
## effect} => {show} 0.1333333 0.8000000 1.500000 4
## [3599] {show,
## effect} => {learn} 0.1333333 0.8000000 1.846154 4
## [3600] {learn,
## effect} => {propos} 0.1333333 0.8000000 1.600000 4
## [3601] {propos,
## effect} => {learn} 0.1333333 1.0000000 2.307692 4
## [3602] {learn,
## effect} => {featur} 0.1333333 0.8000000 1.500000 4
## [3603] {featur,
## effect} => {learn} 0.1333333 1.0000000 2.307692 4
## [3604] {represent,
## effect} => {show} 0.1333333 0.8000000 1.500000 4
## [3605] {show,
## effect} => {represent} 0.1333333 0.8000000 1.600000 4
## [3606] {propos,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [3607] {featur,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [3608] {network,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [3609] {propos,
## effect} => {show} 0.1000000 0.7500000 1.406250 3
## [3610] {model,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [3611] {featur,
## effect} => {show} 0.1000000 0.7500000 1.406250 3
## [3612] {propos,
## effect} => {featur} 0.1000000 0.7500000 1.406250 3
## [3613] {featur,
## effect} => {propos} 0.1000000 0.7500000 1.500000 3
## [3614] {applic,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [3615] {problem,
## applic} => {success} 0.1000000 0.7500000 2.812500 3
## [3616] {problem,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [3617] {applic,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [3618] {perform,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [3619] {applic,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [3620] {applic,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [3621] {make,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [3622] {problem,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [3623] {make,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [3624] {approach,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [3625] {make,
## applic} => {perform} 0.1333333 1.0000000 2.142857 4
## [3626] {make,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [3627] {make,
## applic} => {represent} 0.1000000 0.7500000 1.500000 3
## [3628] {make,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [3629] {make,
## applic} => {model} 0.1000000 0.7500000 1.406250 3
## [3630] {model,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [3631] {problem,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [3632] {approach,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [3633] {problem,
## applic} => {perform} 0.1333333 1.0000000 2.142857 4
## [3634] {problem,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [3635] {learn,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [3636] {problem,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [3637] {represent,
## problem} => {applic} 0.1333333 1.0000000 4.285714 4
## [3638] {problem,
## applic} => {propos} 0.1333333 1.0000000 2.000000 4
## [3639] {propos,
## applic} => {problem} 0.1333333 0.8000000 2.666667 4
## [3640] {problem,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [3641] {featur,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [3642] {result,
## applic} => {work} 0.1000000 0.7500000 1.875000 3
## [3643] {work,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [3644] {result,
## work} => {applic} 0.1000000 0.7500000 3.214286 3
## [3645] {result,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [3646] {perform,
## result} => {applic} 0.1000000 0.7500000 3.214286 3
## [3647] {result,
## applic} => {data} 0.1333333 1.0000000 2.307692 4
## [3648] {data,
## applic} => {result} 0.1333333 0.8000000 2.400000 4
## [3649] {result,
## applic} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3650] {dataset,
## applic} => {result} 0.1000000 0.7500000 2.250000 3
## [3651] {result,
## applic} => {represent} 0.1000000 0.7500000 1.500000 3
## [3652] {method,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [3653] {method,
## applic} => {show} 0.1000000 1.0000000 1.875000 3
## [3654] {show,
## applic} => {method} 0.1000000 0.7500000 2.045455 3
## [3655] {algorithm,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [3656] {algorithm,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [3657] {task,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [3658] {approach,
## applic} => {task} 0.1000000 0.7500000 2.045455 3
## [3659] {task,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [3660] {task,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [3661] {task,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [3662] {approach,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [3663] {approach,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [3664] {approach,
## applic} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3665] {dataset,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [3666] {approach,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [3667] {learn,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [3668] {approach,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [3669] {approach,
## applic} => {propos} 0.1333333 1.0000000 2.000000 4
## [3670] {propos,
## applic} => {approach} 0.1333333 0.8000000 2.000000 4
## [3671] {work,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [3672] {work,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3673] {dataset,
## applic} => {work} 0.1000000 0.7500000 1.875000 3
## [3674] {work,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [3675] {data,
## applic} => {perform} 0.1333333 0.8000000 1.714286 4
## [3676] {dataset,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [3677] {learn,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [3678] {perform,
## applic} => {represent} 0.1666667 0.8333333 1.666667 5
## [3679] {represent,
## applic} => {perform} 0.1666667 0.8333333 1.785714 5
## [3680] {represent,
## perform} => {applic} 0.1666667 0.7142857 3.061224 5
## [3681] {show,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [3682] {propos,
## applic} => {perform} 0.1333333 0.8000000 1.714286 4
## [3683] {model,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [3684] {featur,
## applic} => {perform} 0.1333333 1.0000000 2.142857 4
## [3685] {dataset,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [3686] {data,
## applic} => {represent} 0.1333333 0.8000000 1.600000 4
## [3687] {featur,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [3688] {dataset,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [3689] {learn,
## applic} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3690] {dataset,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [3691] {dataset,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [3692] {learn,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [3693] {learn,
## applic} => {show} 0.1000000 0.7500000 1.406250 3
## [3694] {show,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [3695] {learn,
## applic} => {propos} 0.1333333 1.0000000 2.000000 4
## [3696] {propos,
## applic} => {learn} 0.1333333 0.8000000 1.846154 4
## [3697] {show,
## applic} => {represent} 0.1000000 0.7500000 1.500000 3
## [3698] {represent,
## applic} => {propos} 0.1666667 0.8333333 1.666667 5
## [3699] {propos,
## applic} => {represent} 0.1666667 1.0000000 2.000000 5
## [3700] {featur,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [3701] {show,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [3702] {featur,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [3703] {input,
## recent} => {approach} 0.1000000 1.0000000 2.500000 3
## [3704] {approach,
## recent} => {input} 0.1000000 1.0000000 4.285714 3
## [3705] {input,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [3706] {input,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [3707] {input,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [3708] {input,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [3709] {input,
## make} => {problem} 0.1000000 0.7500000 2.500000 3
## [3710] {input,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [3711] {input,
## make} => {method} 0.1000000 0.7500000 2.045455 3
## [3712] {input,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [3713] {input,
## make} => {approach} 0.1333333 1.0000000 2.500000 4
## [3714] {approach,
## input} => {make} 0.1333333 0.8000000 2.666667 4
## [3715] {input,
## make} => {perform} 0.1000000 0.7500000 1.607143 3
## [3716] {input,
## perform} => {make} 0.1000000 1.0000000 3.333333 3
## [3717] {input,
## make} => {represent} 0.1000000 0.7500000 1.500000 3
## [3718] {input,
## make} => {show} 0.1000000 0.7500000 1.406250 3
## [3719] {make,
## show} => {input} 0.1000000 0.7500000 3.214286 3
## [3720] {input,
## make} => {model} 0.1000000 0.7500000 1.406250 3
## [3721] {input,
## make} => {featur} 0.1000000 0.7500000 1.406250 3
## [3722] {featur,
## input} => {make} 0.1000000 0.7500000 2.500000 3
## [3723] {input,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [3724] {input,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [3725] {input,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [3726] {input,
## paper} => {represent} 0.1000000 0.7500000 1.500000 3
## [3727] {input,
## paper} => {show} 0.1000000 0.7500000 1.406250 3
## [3728] {input,
## paper} => {model} 0.1000000 0.7500000 1.406250 3
## [3729] {input,
## paper} => {network} 0.1000000 0.7500000 1.184211 3
## [3730] {input,
## network} => {paper} 0.1000000 0.7500000 2.250000 3
## [3731] {input,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [3732] {input,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [3733] {input,
## method} => {model} 0.1000000 1.0000000 1.875000 3
## [3734] {input,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [3735] {input,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [3736] {approach,
## input} => {represent} 0.1333333 0.8000000 1.600000 4
## [3737] {input,
## represent} => {approach} 0.1333333 0.8000000 2.000000 4
## [3738] {approach,
## input} => {show} 0.1333333 0.8000000 1.500000 4
## [3739] {input,
## show} => {approach} 0.1333333 0.8000000 2.000000 4
## [3740] {approach,
## input} => {model} 0.1333333 0.8000000 1.500000 4
## [3741] {input,
## model} => {approach} 0.1333333 0.8000000 2.000000 4
## [3742] {approach,
## input} => {featur} 0.1333333 0.8000000 1.500000 4
## [3743] {featur,
## input} => {approach} 0.1333333 1.0000000 2.500000 4
## [3744] {input,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [3745] {input,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [3746] {input,
## represent} => {show} 0.1333333 0.8000000 1.500000 4
## [3747] {input,
## show} => {represent} 0.1333333 0.8000000 1.600000 4
## [3748] {input,
## represent} => {model} 0.1333333 0.8000000 1.500000 4
## [3749] {input,
## model} => {represent} 0.1333333 0.8000000 1.600000 4
## [3750] {featur,
## input} => {represent} 0.1000000 0.7500000 1.500000 3
## [3751] {input,
## network} => {represent} 0.1000000 0.7500000 1.500000 3
## [3752] {input,
## show} => {model} 0.1666667 1.0000000 1.875000 5
## [3753] {input,
## model} => {show} 0.1666667 1.0000000 1.875000 5
## [3754] {featur,
## input} => {show} 0.1000000 0.7500000 1.406250 3
## [3755] {input,
## network} => {show} 0.1000000 0.7500000 1.406250 3
## [3756] {featur,
## input} => {model} 0.1000000 0.7500000 1.406250 3
## [3757] {input,
## network} => {model} 0.1000000 0.7500000 1.406250 3
## [3758] {comput,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [3759] {machin,
## comput} => {recent} 0.1000000 1.0000000 4.285714 3
## [3760] {reduc,
## comput} => {optim} 0.1000000 0.7500000 3.214286 3
## [3761] {comput,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [3762] {reduc,
## optim} => {comput} 0.1000000 0.7500000 3.214286 3
## [3763] {reduc,
## comput} => {problem} 0.1000000 0.7500000 2.500000 3
## [3764] {comput,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [3765] {reduc,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [3766] {reduc,
## comput} => {improv} 0.1000000 0.7500000 2.500000 3
## [3767] {improv,
## comput} => {reduc} 0.1000000 0.7500000 3.214286 3
## [3768] {reduc,
## improv} => {comput} 0.1000000 1.0000000 4.285714 3
## [3769] {reduc,
## comput} => {network} 0.1000000 0.7500000 1.184211 3
## [3770] {comput,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [3771] {comput,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [3772] {comput,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [3773] {improv,
## comput} => {optim} 0.1000000 0.7500000 3.214286 3
## [3774] {improv,
## optim} => {comput} 0.1000000 0.7500000 3.214286 3
## [3775] {comput,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [3776] {improv,
## comput} => {problem} 0.1000000 0.7500000 2.500000 3
## [3777] {improv,
## problem} => {comput} 0.1000000 0.7500000 3.214286 3
## [3778] {improv,
## comput} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3779] {algorithm,
## comput} => {improv} 0.1000000 0.7500000 2.500000 3
## [3780] {improv,
## comput} => {train} 0.1000000 0.7500000 1.875000 3
## [3781] {train,
## comput} => {improv} 0.1000000 1.0000000 3.333333 3
## [3782] {improv,
## comput} => {model} 0.1000000 0.7500000 1.406250 3
## [3783] {model,
## comput} => {improv} 0.1000000 0.7500000 2.500000 3
## [3784] {improv,
## comput} => {network} 0.1000000 0.7500000 1.184211 3
## [3785] {neural,
## comput} => {network} 0.1000000 1.0000000 1.578947 3
## [3786] {algorithm,
## comput} => {train} 0.1000000 0.7500000 1.875000 3
## [3787] {train,
## comput} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3788] {algorithm,
## comput} => {show} 0.1000000 0.7500000 1.406250 3
## [3789] {show,
## comput} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3790] {algorithm,
## comput} => {model} 0.1000000 0.7500000 1.406250 3
## [3791] {model,
## comput} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3792] {comput,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [3793] {problem,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [3794] {machin,
## problem} => {recent} 0.1000000 1.0000000 4.285714 3
## [3795] {algorithm,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [3796] {machin,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [3797] {machin,
## learn} => {recent} 0.1000000 0.7500000 3.214286 3
## [3798] {machin,
## recent} => {show} 0.1333333 0.8000000 1.500000 4
## [3799] {machin,
## show} => {recent} 0.1333333 0.8000000 3.428571 4
## [3800] {machin,
## recent} => {model} 0.1333333 0.8000000 1.500000 4
## [3801] {machin,
## model} => {recent} 0.1333333 0.8000000 3.428571 4
## [3802] {featur,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [3803] {recent,
## signific} => {problem} 0.1000000 1.0000000 3.333333 3
## [3804] {problem,
## recent} => {signific} 0.1000000 0.7500000 2.812500 3
## [3805] {problem,
## signific} => {recent} 0.1000000 1.0000000 4.285714 3
## [3806] {recent,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [3807] {recent,
## signific} => {model} 0.1000000 1.0000000 1.875000 3
## [3808] {model,
## signific} => {recent} 0.1000000 1.0000000 4.285714 3
## [3809] {problem,
## recent} => {method} 0.1000000 0.7500000 2.045455 3
## [3810] {method,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [3811] {problem,
## recent} => {perform} 0.1000000 0.7500000 1.607143 3
## [3812] {perform,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [3813] {problem,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [3814] {problem,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [3815] {problem,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [3816] {method,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [3817] {method,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [3818] {algorithm,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [3819] {algorithm,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [3820] {algorithm,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [3821] {algorithm,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [3822] {algorithm,
## recent} => {featur} 0.1000000 0.7500000 1.406250 3
## [3823] {featur,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3824] {approach,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [3825] {approach,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [3826] {approach,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [3827] {perform,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [3828] {perform,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [3829] {perform,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [3830] {represent,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [3831] {learn,
## recent} => {show} 0.1666667 1.0000000 1.875000 5
## [3832] {show,
## recent} => {learn} 0.1666667 0.8333333 1.923077 5
## [3833] {learn,
## recent} => {model} 0.1666667 1.0000000 1.875000 5
## [3834] {model,
## recent} => {learn} 0.1666667 0.8333333 1.923077 5
## [3835] {learn,
## recent} => {featur} 0.1333333 0.8000000 1.500000 4
## [3836] {featur,
## recent} => {learn} 0.1333333 1.0000000 2.307692 4
## [3837] {represent,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [3838] {represent,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [3839] {show,
## recent} => {model} 0.2000000 1.0000000 1.875000 6
## [3840] {model,
## recent} => {show} 0.2000000 1.0000000 1.875000 6
## [3841] {featur,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [3842] {featur,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [3843] {machin,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [3844] {machin,
## train} => {signific} 0.1000000 0.7500000 2.812500 3
## [3845] {machin,
## make} => {classif} 0.1000000 1.0000000 3.750000 3
## [3846] {classif,
## machin} => {make} 0.1000000 0.7500000 2.500000 3
## [3847] {classif,
## make} => {machin} 0.1000000 1.0000000 4.285714 3
## [3848] {machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [3849] {machin,
## method} => {make} 0.1000000 0.7500000 2.500000 3
## [3850] {machin,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [3851] {approach,
## machin} => {make} 0.1000000 1.0000000 3.333333 3
## [3852] {machin,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [3853] {classif,
## machin} => {paper} 0.1000000 0.7500000 2.250000 3
## [3854] {machin,
## paper} => {classif} 0.1000000 1.0000000 3.750000 3
## [3855] {classif,
## paper} => {machin} 0.1000000 1.0000000 4.285714 3
## [3856] {classif,
## machin} => {method} 0.1000000 0.7500000 2.045455 3
## [3857] {machin,
## method} => {classif} 0.1000000 0.7500000 2.812500 3
## [3858] {classif,
## machin} => {task} 0.1000000 0.7500000 2.045455 3
## [3859] {machin,
## task} => {classif} 0.1000000 0.7500000 2.812500 3
## [3860] {classif,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [3861] {classif,
## machin} => {train} 0.1000000 0.7500000 1.875000 3
## [3862] {machin,
## train} => {classif} 0.1000000 0.7500000 2.812500 3
## [3863] {classif,
## train} => {machin} 0.1000000 0.7500000 3.214286 3
## [3864] {classif,
## machin} => {approach} 0.1000000 0.7500000 1.875000 3
## [3865] {approach,
## machin} => {classif} 0.1000000 1.0000000 3.750000 3
## [3866] {classif,
## machin} => {learn} 0.1000000 0.7500000 1.730769 3
## [3867] {machin,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [3868] {classif,
## machin} => {show} 0.1000000 0.7500000 1.406250 3
## [3869] {classif,
## machin} => {model} 0.1000000 0.7500000 1.406250 3
## [3870] {classif,
## machin} => {featur} 0.1333333 1.0000000 1.875000 4
## [3871] {featur,
## machin} => {classif} 0.1333333 0.8000000 3.000000 4
## [3872] {machin,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [3873] {machin,
## problem} => {model} 0.1000000 1.0000000 1.875000 3
## [3874] {machin,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [3875] {machin,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [3876] {machin,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [3877] {machin,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [3878] {machin,
## paper} => {featur} 0.1000000 1.0000000 1.875000 3
## [3879] {machin,
## method} => {train} 0.1000000 0.7500000 1.875000 3
## [3880] {machin,
## train} => {method} 0.1000000 0.7500000 2.045455 3
## [3881] {method,
## train} => {machin} 0.1000000 0.7500000 3.214286 3
## [3882] {machin,
## method} => {approach} 0.1000000 0.7500000 1.875000 3
## [3883] {approach,
## machin} => {method} 0.1000000 1.0000000 2.727273 3
## [3884] {machin,
## method} => {show} 0.1000000 0.7500000 1.406250 3
## [3885] {machin,
## method} => {model} 0.1000000 0.7500000 1.406250 3
## [3886] {machin,
## method} => {featur} 0.1000000 0.7500000 1.406250 3
## [3887] {machin,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [3888] {machin,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [3889] {machin,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [3890] {machin,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [3891] {machin,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [3892] {data,
## machin} => {task} 0.1000000 1.0000000 2.727273 3
## [3893] {machin,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [3894] {machin,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [3895] {machin,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [3896] {machin,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [3897] {machin,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [3898] {machin,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [3899] {machin,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [3900] {featur,
## machin} => {task} 0.1333333 0.8000000 2.181818 4
## [3901] {machin,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [3902] {machin,
## train} => {model} 0.1000000 0.7500000 1.406250 3
## [3903] {machin,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [3904] {approach,
## machin} => {featur} 0.1000000 1.0000000 1.875000 3
## [3905] {data,
## machin} => {show} 0.1000000 1.0000000 1.875000 3
## [3906] {data,
## machin} => {model} 0.1000000 1.0000000 1.875000 3
## [3907] {data,
## machin} => {featur} 0.1000000 1.0000000 1.875000 3
## [3908] {machin,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [3909] {machin,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [3910] {machin,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [3911] {featur,
## machin} => {learn} 0.1333333 0.8000000 1.846154 4
## [3912] {machin,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [3913] {machin,
## show} => {model} 0.1666667 1.0000000 1.875000 5
## [3914] {machin,
## model} => {show} 0.1666667 1.0000000 1.875000 5
## [3915] {machin,
## show} => {featur} 0.1333333 0.8000000 1.500000 4
## [3916] {featur,
## machin} => {show} 0.1333333 0.8000000 1.500000 4
## [3917] {machin,
## model} => {featur} 0.1333333 0.8000000 1.500000 4
## [3918] {featur,
## machin} => {model} 0.1333333 0.8000000 1.500000 4
## [3919] {experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3920] {architectur,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [3921] {experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [3922] {classif,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [3923] {classif,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [3924] {experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3925] {dataset,
## process} => {experi} 0.1000000 0.7500000 2.812500 3
## [3926] {dataset,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [3927] {experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [3928] {process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [3929] {experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [3930] {classif,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3931] {classif,
## architectur} => {process} 0.1000000 0.7500000 3.750000 3
## [3932] {process,
## recognit} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3933] {architectur,
## recognit} => {process} 0.1000000 1.0000000 5.000000 3
## [3934] {improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3935] {architectur,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [3936] {process,
## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3937] {architectur,
## result} => {process} 0.1000000 0.7500000 3.750000 3
## [3938] {neural,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3939] {architectur,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [3940] {architectur,
## process} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [3941] {algorithm,
## process} => {architectur} 0.1333333 0.8000000 3.000000 4
## [3942] {algorithm,
## architectur} => {process} 0.1333333 1.0000000 5.000000 4
## [3943] {process,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3944] {perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3945] {data,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3946] {data,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [3947] {architectur,
## process} => {dataset} 0.1333333 0.8000000 1.846154 4
## [3948] {dataset,
## process} => {architectur} 0.1333333 1.0000000 3.750000 4
## [3949] {architectur,
## dataset} => {process} 0.1333333 0.8000000 4.000000 4
## [3950] {show,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3951] {show,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [3952] {process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3953] {model,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3954] {model,
## architectur} => {process} 0.1000000 0.7500000 3.750000 3
## [3955] {architectur,
## process} => {featur} 0.1333333 0.8000000 1.500000 4
## [3956] {featur,
## process} => {architectur} 0.1333333 0.8000000 3.000000 4
## [3957] {architectur,
## process} => {network} 0.1333333 0.8000000 1.263158 4
## [3958] {network,
## process} => {architectur} 0.1333333 0.8000000 3.000000 4
## [3959] {classif,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3960] {dataset,
## process} => {classif} 0.1000000 0.7500000 2.812500 3
## [3961] {classif,
## dataset} => {process} 0.1000000 0.7500000 3.750000 3
## [3962] {classif,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [3963] {process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [3964] {classif,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [3965] {process,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [3966] {model,
## process} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3967] {model,
## recognit} => {process} 0.1000000 0.7500000 3.750000 3
## [3968] {process,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [3969] {improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [3970] {neural,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [3971] {improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3972] {improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [3973] {perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [3974] {improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3975] {dataset,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [3976] {improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [3977] {process,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3978] {process,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [3979] {neural,
## process} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [3980] {algorithm,
## process} => {neural} 0.1333333 0.8000000 2.400000 4
## [3981] {algorithm,
## neural} => {process} 0.1333333 0.8000000 4.000000 4
## [3982] {neural,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [3983] {approach,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [3984] {neural,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [3985] {process,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [3986] {neural,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [3987] {perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [3988] {neural,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [3989] {neural,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3990] {dataset,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [3991] {neural,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [3992] {show,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [3993] {neural,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [3994] {neural,
## process} => {network} 0.1333333 1.0000000 1.578947 4
## [3995] {network,
## process} => {neural} 0.1333333 0.8000000 2.400000 4
## [3996] {approach,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3997] {process,
## work} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3998] {algorithm,
## work} => {process} 0.1000000 0.7500000 3.750000 3
## [3999] {perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [4000] {dataset,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4001] {algorithm,
## dataset} => {process} 0.1000000 0.7500000 3.750000 3
## [4002] {represent,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [4003] {represent,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [4004] {algorithm,
## process} => {show} 0.1333333 0.8000000 1.500000 4
## [4005] {show,
## process} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [4006] {model,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4007] {algorithm,
## process} => {featur} 0.1333333 0.8000000 1.500000 4
## [4008] {featur,
## process} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [4009] {algorithm,
## process} => {network} 0.1333333 0.8000000 1.263158 4
## [4010] {network,
## process} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [4011] {approach,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [4012] {show,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [4013] {approach,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [4014] {process,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4015] {dataset,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [4016] {process,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [4017] {process,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [4018] {network,
## process} => {work} 0.1333333 0.8000000 2.000000 4
## [4019] {perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4020] {dataset,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [4021] {perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [4022] {data,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [4023] {dataset,
## process} => {propos} 0.1000000 0.7500000 1.500000 3
## [4024] {process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4025] {dataset,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [4026] {dataset,
## process} => {network} 0.1333333 1.0000000 1.578947 4
## [4027] {network,
## process} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4028] {process,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [4029] {model,
## process} => {learn} 0.1000000 0.7500000 1.730769 3
## [4030] {process,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [4031] {represent,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [4032] {show,
## process} => {model} 0.1000000 0.7500000 1.406250 3
## [4033] {model,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [4034] {show,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [4035] {show,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [4036] {process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [4037] {model,
## process} => {featur} 0.1333333 1.0000000 1.875000 4
## [4038] {featur,
## process} => {model} 0.1333333 0.8000000 1.500000 4
## [4039] {model,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [4040] {featur,
## process} => {network} 0.1333333 0.8000000 1.263158 4
## [4041] {network,
## process} => {featur} 0.1333333 0.8000000 1.500000 4
## [4042] {experi,
## demonstr} => {network} 0.1000000 1.0000000 1.578947 3
## [4043] {network,
## demonstr} => {experi} 0.1000000 0.7500000 2.812500 3
## [4044] {classif,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4045] {dataset,
## demonstr} => {classif} 0.1000000 0.7500000 2.812500 3
## [4046] {classif,
## dataset} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [4047] {demonstr,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [4048] {approach,
## demonstr} => {problem} 0.1000000 1.0000000 3.333333 3
## [4049] {demonstr,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [4050] {perform,
## demonstr} => {problem} 0.1000000 0.7500000 2.500000 3
## [4051] {demonstr,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4052] {dataset,
## demonstr} => {problem} 0.1000000 0.7500000 2.500000 3
## [4053] {dataset,
## problem} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [4054] {improv,
## demonstr} => {perform} 0.1000000 1.0000000 2.142857 3
## [4055] {perform,
## demonstr} => {improv} 0.1000000 0.7500000 2.500000 3
## [4056] {improv,
## demonstr} => {show} 0.1000000 1.0000000 1.875000 3
## [4057] {show,
## demonstr} => {improv} 0.1000000 1.0000000 3.333333 3
## [4058] {task,
## demonstr} => {data} 0.1000000 1.0000000 2.307692 3
## [4059] {data,
## demonstr} => {task} 0.1000000 1.0000000 2.727273 3
## [4060] {approach,
## demonstr} => {perform} 0.1000000 1.0000000 2.142857 3
## [4061] {perform,
## demonstr} => {approach} 0.1000000 0.7500000 1.875000 3
## [4062] {approach,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4063] {dataset,
## demonstr} => {approach} 0.1000000 0.7500000 1.875000 3
## [4064] {demonstr,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [4065] {perform,
## demonstr} => {work} 0.1000000 0.7500000 1.875000 3
## [4066] {demonstr,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4067] {dataset,
## demonstr} => {work} 0.1000000 0.7500000 1.875000 3
## [4068] {demonstr,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [4069] {propos,
## demonstr} => {work} 0.1333333 1.0000000 2.500000 4
## [4070] {perform,
## demonstr} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4071] {dataset,
## demonstr} => {perform} 0.1000000 0.7500000 1.607143 3
## [4072] {perform,
## demonstr} => {show} 0.1000000 0.7500000 1.406250 3
## [4073] {show,
## demonstr} => {perform} 0.1000000 1.0000000 2.142857 3
## [4074] {perform,
## demonstr} => {propos} 0.1000000 0.7500000 1.500000 3
## [4075] {propos,
## demonstr} => {perform} 0.1000000 0.7500000 1.607143 3
## [4076] {dataset,
## demonstr} => {learn} 0.1000000 0.7500000 1.730769 3
## [4077] {demonstr,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4078] {dataset,
## demonstr} => {propos} 0.1000000 0.7500000 1.500000 3
## [4079] {propos,
## demonstr} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4080] {dataset,
## demonstr} => {model} 0.1000000 0.7500000 1.406250 3
## [4081] {model,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4082] {dataset,
## demonstr} => {featur} 0.1000000 0.7500000 1.406250 3
## [4083] {featur,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4084] {demonstr,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [4085] {model,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [4086] {demonstr,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [4087] {featur,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [4088] {model,
## demonstr} => {featur} 0.1000000 1.0000000 1.875000 3
## [4089] {featur,
## demonstr} => {model} 0.1000000 1.0000000 1.875000 3
## [4090] {reduc,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [4091] {reduc,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [4092] {task,
## achiev} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4093] {reduc,
## achiev} => {data} 0.1000000 1.0000000 2.307692 3
## [4094] {data,
## reduc} => {achiev} 0.1000000 0.7500000 3.214286 3
## [4095] {data,
## achiev} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4096] {reduc,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [4097] {reduc,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4098] {reduc,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [4099] {reduc,
## optim} => {improv} 0.1000000 0.7500000 2.500000 3
## [4100] {reduc,
## improv} => {optim} 0.1000000 1.0000000 4.285714 3
## [4101] {improv,
## optim} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4102] {reduc,
## optim} => {method} 0.1000000 0.7500000 2.045455 3
## [4103] {method,
## reduc} => {optim} 0.1000000 0.7500000 3.214286 3
## [4104] {method,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [4105] {reduc,
## optim} => {work} 0.1000000 0.7500000 1.875000 3
## [4106] {reduc,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [4107] {optim,
## work} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4108] {reduc,
## optim} => {show} 0.1000000 0.7500000 1.406250 3
## [4109] {show,
## optim} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4110] {reduc,
## optim} => {model} 0.1000000 0.7500000 1.406250 3
## [4111] {model,
## reduc} => {optim} 0.1000000 0.7500000 3.214286 3
## [4112] {model,
## optim} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4113] {reduc,
## optim} => {network} 0.1000000 0.7500000 1.184211 3
## [4114] {network,
## optim} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4115] {reduc,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [4116] {reduc,
## improv} => {problem} 0.1000000 1.0000000 3.333333 3
## [4117] {improv,
## problem} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4118] {paper,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [4119] {reduc,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [4120] {paper,
## reduc} => {data} 0.1000000 1.0000000 2.307692 3
## [4121] {data,
## reduc} => {paper} 0.1000000 0.7500000 2.250000 3
## [4122] {paper,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [4123] {method,
## reduc} => {train} 0.1000000 0.7500000 1.875000 3
## [4124] {reduc,
## train} => {method} 0.1000000 0.7500000 2.045455 3
## [4125] {method,
## train} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4126] {method,
## reduc} => {approach} 0.1000000 0.7500000 1.875000 3
## [4127] {approach,
## reduc} => {method} 0.1000000 1.0000000 2.727273 3
## [4128] {method,
## reduc} => {show} 0.1333333 1.0000000 1.875000 4
## [4129] {reduc,
## show} => {method} 0.1333333 0.8000000 2.181818 4
## [4130] {method,
## reduc} => {model} 0.1000000 0.7500000 1.406250 3
## [4131] {model,
## reduc} => {method} 0.1000000 0.7500000 2.045455 3
## [4132] {method,
## reduc} => {network} 0.1000000 0.7500000 1.184211 3
## [4133] {reduc,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [4134] {data,
## reduc} => {task} 0.1333333 1.0000000 2.727273 4
## [4135] {reduc,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [4136] {reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [4137] {reduc,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [4138] {reduc,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [4139] {featur,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [4140] {reduc,
## task} => {network} 0.1333333 1.0000000 1.578947 4
## [4141] {reduc,
## train} => {show} 0.1333333 1.0000000 1.875000 4
## [4142] {reduc,
## show} => {train} 0.1333333 0.8000000 2.000000 4
## [4143] {reduc,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [4144] {approach,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [4145] {approach,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [4146] {reduc,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [4147] {data,
## reduc} => {represent} 0.1000000 0.7500000 1.500000 3
## [4148] {reduc,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [4149] {data,
## reduc} => {show} 0.1000000 0.7500000 1.406250 3
## [4150] {data,
## reduc} => {featur} 0.1000000 0.7500000 1.406250 3
## [4151] {featur,
## reduc} => {data} 0.1000000 1.0000000 2.307692 3
## [4152] {data,
## reduc} => {network} 0.1333333 1.0000000 1.578947 4
## [4153] {reduc,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [4154] {reduc,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [4155] {reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [4156] {reduc,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [4157] {featur,
## reduc} => {represent} 0.1000000 1.0000000 2.000000 3
## [4158] {reduc,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [4159] {model,
## reduc} => {show} 0.1000000 0.7500000 1.406250 3
## [4160] {featur,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [4161] {reduc,
## show} => {network} 0.1333333 0.8000000 1.263158 4
## [4162] {model,
## reduc} => {network} 0.1000000 0.7500000 1.184211 3
## [4163] {featur,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [4164] {paper,
## achiev} => {task} 0.1000000 0.7500000 2.045455 3
## [4165] {task,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [4166] {paper,
## achiev} => {train} 0.1000000 0.7500000 1.875000 3
## [4167] {train,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [4168] {paper,
## achiev} => {data} 0.1000000 0.7500000 1.730769 3
## [4169] {data,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [4170] {paper,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [4171] {achiev,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [4172] {paper,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [4173] {represent,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [4174] {paper,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4175] {paper,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [4176] {achiev,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4177] {achiev,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [4178] {achiev,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [4179] {achiev,
## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [4180] {achiev,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4181] {achiev,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [4182] {achiev,
## improv} => {model} 0.1000000 1.0000000 1.875000 3
## [4183] {model,
## achiev} => {improv} 0.1000000 0.7500000 2.500000 3
## [4184] {achiev,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [4185] {achiev,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [4186] {achiev,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [4187] {train,
## achiev} => {result} 0.1000000 0.7500000 2.250000 3
## [4188] {achiev,
## result} => {approach} 0.1000000 0.7500000 1.875000 3
## [4189] {approach,
## achiev} => {result} 0.1000000 0.7500000 2.250000 3
## [4190] {achiev,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4191] {achiev,
## result} => {propos} 0.1000000 0.7500000 1.500000 3
## [4192] {achiev,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [4193] {achiev,
## result} => {network} 0.1333333 1.0000000 1.578947 4
## [4194] {achiev,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [4195] {train,
## achiev} => {neural} 0.1000000 0.7500000 2.250000 3
## [4196] {achiev,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [4197] {approach,
## achiev} => {neural} 0.1000000 0.7500000 2.250000 3
## [4198] {achiev,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4199] {achiev,
## neural} => {propos} 0.1333333 1.0000000 2.000000 4
## [4200] {achiev,
## propos} => {neural} 0.1333333 0.8000000 2.400000 4
## [4201] {achiev,
## neural} => {model} 0.1000000 0.7500000 1.406250 3
## [4202] {model,
## achiev} => {neural} 0.1000000 0.7500000 2.250000 3
## [4203] {achiev,
## neural} => {featur} 0.1000000 0.7500000 1.406250 3
## [4204] {achiev,
## neural} => {network} 0.1000000 0.7500000 1.184211 3
## [4205] {method,
## achiev} => {approach} 0.1000000 1.0000000 2.500000 3
## [4206] {approach,
## achiev} => {method} 0.1000000 0.7500000 2.045455 3
## [4207] {method,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [4208] {method,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [4209] {method,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [4210] {task,
## achiev} => {data} 0.1000000 0.7500000 1.730769 3
## [4211] {data,
## achiev} => {task} 0.1000000 0.7500000 2.045455 3
## [4212] {task,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [4213] {achiev,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [4214] {task,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [4215] {represent,
## achiev} => {task} 0.1000000 0.7500000 2.045455 3
## [4216] {task,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4217] {task,
## achiev} => {network} 0.1333333 1.0000000 1.578947 4
## [4218] {train,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4219] {train,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [4220] {achiev,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [4221] {train,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [4222] {represent,
## achiev} => {train} 0.1000000 0.7500000 1.875000 3
## [4223] {train,
## achiev} => {propos} 0.1000000 0.7500000 1.500000 3
## [4224] {train,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4225] {train,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [4226] {approach,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4227] {approach,
## achiev} => {propos} 0.1333333 1.0000000 2.000000 4
## [4228] {achiev,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [4229] {approach,
## achiev} => {model} 0.1000000 0.7500000 1.406250 3
## [4230] {model,
## achiev} => {approach} 0.1000000 0.7500000 1.875000 3
## [4231] {approach,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4232] {approach,
## achiev} => {network} 0.1333333 1.0000000 1.578947 4
## [4233] {data,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4234] {data,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [4235] {achiev,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [4236] {data,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [4237] {represent,
## achiev} => {data} 0.1000000 0.7500000 1.730769 3
## [4238] {data,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4239] {data,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [4240] {achiev,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4241] {represent,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4242] {show,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4243] {achiev,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [4244] {achiev,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4245] {achiev,
## dataset} => {model} 0.1333333 0.8000000 1.500000 4
## [4246] {model,
## achiev} => {dataset} 0.1333333 1.0000000 2.307692 4
## [4247] {achiev,
## dataset} => {featur} 0.1333333 0.8000000 1.500000 4
## [4248] {featur,
## achiev} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4249] {achiev,
## dataset} => {network} 0.1333333 0.8000000 1.263158 4
## [4250] {achiev,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [4251] {represent,
## achiev} => {learn} 0.1333333 1.0000000 2.307692 4
## [4252] {achiev,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [4253] {achiev,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [4254] {featur,
## achiev} => {learn} 0.1333333 0.8000000 1.846154 4
## [4255] {achiev,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [4256] {represent,
## achiev} => {propos} 0.1000000 0.7500000 1.500000 3
## [4257] {represent,
## achiev} => {featur} 0.1333333 1.0000000 1.875000 4
## [4258] {featur,
## achiev} => {represent} 0.1333333 0.8000000 1.600000 4
## [4259] {represent,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [4260] {show,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [4261] {show,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [4262] {achiev,
## propos} => {model} 0.1333333 0.8000000 1.500000 4
## [4263] {model,
## achiev} => {propos} 0.1333333 1.0000000 2.000000 4
## [4264] {achiev,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [4265] {featur,
## achiev} => {propos} 0.1333333 0.8000000 1.600000 4
## [4266] {achiev,
## propos} => {network} 0.1333333 0.8000000 1.263158 4
## [4267] {model,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4268] {model,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [4269] {featur,
## achiev} => {network} 0.1333333 0.8000000 1.263158 4
## [4270] {optim,
## signific} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [4271] {algorithm,
## signific} => {optim} 0.1000000 1.0000000 4.285714 3
## [4272] {optim,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4273] {train,
## optim} => {signific} 0.1000000 0.7500000 2.812500 3
## [4274] {object,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4275] {object,
## problem} => {optim} 0.1000000 0.7500000 3.214286 3
## [4276] {object,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4277] {algorithm,
## object} => {optim} 0.1000000 0.7500000 3.214286 3
## [4278] {object,
## optim} => {task} 0.1000000 0.7500000 2.045455 3
## [4279] {task,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [4280] {task,
## object} => {optim} 0.1000000 0.7500000 3.214286 3
## [4281] {object,
## optim} => {data} 0.1000000 0.7500000 1.730769 3
## [4282] {data,
## optim} => {object} 0.1000000 0.7500000 2.812500 3
## [4283] {data,
## object} => {optim} 0.1000000 0.7500000 3.214286 3
## [4284] {object,
## optim} => {show} 0.1000000 0.7500000 1.406250 3
## [4285] {show,
## optim} => {object} 0.1000000 0.7500000 2.812500 3
## [4286] {object,
## optim} => {propos} 0.1000000 0.7500000 1.500000 3
## [4287] {propos,
## optim} => {object} 0.1000000 0.7500000 2.812500 3
## [4288] {object,
## optim} => {model} 0.1000000 0.7500000 1.406250 3
## [4289] {model,
## optim} => {object} 0.1000000 0.7500000 2.812500 3
## [4290] {object,
## optim} => {featur} 0.1000000 0.7500000 1.406250 3
## [4291] {featur,
## optim} => {object} 0.1000000 0.7500000 2.812500 3
## [4292] {architectur,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [4293] {improv,
## optim} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4294] {architectur,
## improv} => {optim} 0.1000000 0.7500000 3.214286 3
## [4295] {architectur,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [4296] {optim,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4297] {architectur,
## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [4298] {architectur,
## optim} => {network} 0.1000000 1.0000000 1.578947 3
## [4299] {network,
## optim} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4300] {improv,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4301] {improv,
## problem} => {optim} 0.1000000 0.7500000 3.214286 3
## [4302] {optim,
## problem} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [4303] {algorithm,
## optim} => {problem} 0.1333333 0.8000000 2.666667 4
## [4304] {algorithm,
## problem} => {optim} 0.1333333 1.0000000 4.285714 4
## [4305] {train,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4306] {train,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [4307] {optim,
## problem} => {perform} 0.1333333 0.8000000 1.714286 4
## [4308] {perform,
## optim} => {problem} 0.1333333 0.8000000 2.666667 4
## [4309] {show,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4310] {propos,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4311] {model,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4312] {improv,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4313] {improv,
## optim} => {train} 0.1000000 0.7500000 1.875000 3
## [4314] {train,
## optim} => {improv} 0.1000000 0.7500000 2.500000 3
## [4315] {improv,
## optim} => {work} 0.1000000 0.7500000 1.875000 3
## [4316] {optim,
## work} => {improv} 0.1000000 0.7500000 2.500000 3
## [4317] {improv,
## work} => {optim} 0.1000000 0.7500000 3.214286 3
## [4318] {improv,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4319] {improv,
## optim} => {network} 0.1000000 0.7500000 1.184211 3
## [4320] {network,
## optim} => {improv} 0.1000000 0.7500000 2.500000 3
## [4321] {method,
## optim} => {show} 0.1000000 1.0000000 1.875000 3
## [4322] {show,
## optim} => {method} 0.1000000 0.7500000 2.045455 3
## [4323] {algorithm,
## optim} => {train} 0.1333333 0.8000000 2.000000 4
## [4324] {train,
## optim} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [4325] {algorithm,
## optim} => {perform} 0.1333333 0.8000000 1.714286 4
## [4326] {perform,
## optim} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [4327] {data,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4328] {show,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4329] {propos,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4330] {featur,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4331] {task,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [4332] {data,
## optim} => {task} 0.1000000 0.7500000 2.045455 3
## [4333] {task,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [4334] {propos,
## optim} => {task} 0.1000000 0.7500000 2.045455 3
## [4335] {task,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [4336] {featur,
## optim} => {task} 0.1000000 0.7500000 2.045455 3
## [4337] {train,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4338] {train,
## optim} => {show} 0.1000000 0.7500000 1.406250 3
## [4339] {show,
## optim} => {train} 0.1000000 0.7500000 1.875000 3
## [4340] {approach,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [4341] {propos,
## optim} => {approach} 0.1000000 0.7500000 1.875000 3
## [4342] {optim,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [4343] {optim,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4344] {dataset,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [4345] {optim,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [4346] {network,
## optim} => {work} 0.1333333 1.0000000 2.500000 4
## [4347] {data,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4348] {propos,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4349] {featur,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4350] {network,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4351] {data,
## optim} => {represent} 0.1000000 0.7500000 1.500000 3
## [4352] {represent,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [4353] {data,
## optim} => {propos} 0.1000000 0.7500000 1.500000 3
## [4354] {propos,
## optim} => {data} 0.1000000 0.7500000 1.730769 3
## [4355] {data,
## optim} => {featur} 0.1333333 1.0000000 1.875000 4
## [4356] {featur,
## optim} => {data} 0.1333333 1.0000000 2.307692 4
## [4357] {dataset,
## optim} => {network} 0.1000000 1.0000000 1.578947 3
## [4358] {network,
## optim} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4359] {represent,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [4360] {featur,
## optim} => {represent} 0.1000000 0.7500000 1.500000 3
## [4361] {show,
## optim} => {propos} 0.1000000 0.7500000 1.500000 3
## [4362] {propos,
## optim} => {show} 0.1000000 0.7500000 1.406250 3
## [4363] {show,
## optim} => {model} 0.1000000 0.7500000 1.406250 3
## [4364] {model,
## optim} => {show} 0.1000000 0.7500000 1.406250 3
## [4365] {propos,
## optim} => {featur} 0.1000000 0.7500000 1.406250 3
## [4366] {featur,
## optim} => {propos} 0.1000000 0.7500000 1.500000 3
## [4367] {object,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4368] {train,
## object} => {signific} 0.1000000 1.0000000 3.750000 3
## [4369] {object,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [4370] {architectur,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [4371] {architectur,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [4372] {network,
## signific} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4373] {problem,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [4374] {problem,
## signific} => {model} 0.1000000 1.0000000 1.875000 3
## [4375] {model,
## signific} => {problem} 0.1000000 1.0000000 3.333333 3
## [4376] {paper,
## signific} => {task} 0.1000000 0.7500000 2.045455 3
## [4377] {task,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [4378] {paper,
## signific} => {train} 0.1000000 0.7500000 1.875000 3
## [4379] {paper,
## signific} => {learn} 0.1333333 1.0000000 2.307692 4
## [4380] {learn,
## signific} => {paper} 0.1333333 1.0000000 3.000000 4
## [4381] {paper,
## learn} => {signific} 0.1333333 0.8000000 3.000000 4
## [4382] {paper,
## signific} => {represent} 0.1000000 0.7500000 1.500000 3
## [4383] {paper,
## signific} => {show} 0.1000000 0.7500000 1.406250 3
## [4384] {paper,
## signific} => {propos} 0.1000000 0.7500000 1.500000 3
## [4385] {paper,
## propos} => {signific} 0.1000000 0.7500000 2.812500 3
## [4386] {paper,
## signific} => {featur} 0.1000000 0.7500000 1.406250 3
## [4387] {improv,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4388] {result,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4389] {result,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [4390] {result,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [4391] {result,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [4392] {network,
## signific} => {result} 0.1000000 0.7500000 2.250000 3
## [4393] {method,
## signific} => {propos} 0.1000000 0.7500000 1.500000 3
## [4394] {algorithm,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4395] {task,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4396] {task,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [4397] {learn,
## signific} => {task} 0.1000000 0.7500000 2.045455 3
## [4398] {task,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [4399] {data,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4400] {learn,
## signific} => {train} 0.1000000 0.7500000 1.875000 3
## [4401] {represent,
## signific} => {train} 0.1333333 0.8000000 2.000000 4
## [4402] {show,
## signific} => {train} 0.1333333 0.8000000 2.000000 4
## [4403] {featur,
## signific} => {train} 0.1333333 0.8000000 2.000000 4
## [4404] {network,
## signific} => {train} 0.1000000 0.7500000 1.875000 3
## [4405] {work,
## signific} => {perform} 0.1000000 1.0000000 2.142857 3
## [4406] {dataset,
## signific} => {perform} 0.1000000 0.7500000 1.607143 3
## [4407] {perform,
## signific} => {propos} 0.1333333 0.8000000 1.600000 4
## [4408] {propos,
## signific} => {perform} 0.1333333 0.8000000 1.714286 4
## [4409] {data,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [4410] {dataset,
## signific} => {represent} 0.1000000 0.7500000 1.500000 3
## [4411] {dataset,
## signific} => {featur} 0.1000000 0.7500000 1.406250 3
## [4412] {dataset,
## signific} => {network} 0.1000000 0.7500000 1.184211 3
## [4413] {network,
## signific} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4414] {learn,
## signific} => {represent} 0.1000000 0.7500000 1.500000 3
## [4415] {learn,
## signific} => {show} 0.1000000 0.7500000 1.406250 3
## [4416] {learn,
## signific} => {propos} 0.1000000 0.7500000 1.500000 3
## [4417] {learn,
## signific} => {featur} 0.1000000 0.7500000 1.406250 3
## [4418] {network,
## signific} => {represent} 0.1000000 0.7500000 1.500000 3
## [4419] {model,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [4420] {featur,
## signific} => {network} 0.1333333 0.8000000 1.263158 4
## [4421] {network,
## signific} => {featur} 0.1333333 1.0000000 1.875000 4
## [4422] {experi,
## success} => {paper} 0.1000000 1.0000000 3.000000 3
## [4423] {paper,
## success} => {experi} 0.1000000 1.0000000 3.750000 3
## [4424] {paper,
## experi} => {success} 0.1000000 0.7500000 2.812500 3
## [4425] {object,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [4426] {represent,
## object} => {success} 0.1000000 0.7500000 2.812500 3
## [4427] {object,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [4428] {object,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [4429] {make,
## success} => {approach} 0.1000000 1.0000000 2.500000 3
## [4430] {approach,
## success} => {make} 0.1000000 0.7500000 2.500000 3
## [4431] {make,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [4432] {make,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [4433] {problem,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [4434] {perform,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [4435] {problem,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [4436] {represent,
## problem} => {success} 0.1000000 0.7500000 2.812500 3
## [4437] {problem,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [4438] {method,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [4439] {approach,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [4440] {method,
## success} => {learn} 0.1333333 1.0000000 2.307692 4
## [4441] {learn,
## success} => {method} 0.1333333 0.8000000 2.181818 4
## [4442] {method,
## learn} => {success} 0.1333333 0.8000000 3.000000 4
## [4443] {method,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [4444] {method,
## represent} => {success} 0.1333333 0.8000000 3.000000 4
## [4445] {method,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [4446] {show,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [4447] {method,
## success} => {propos} 0.1333333 1.0000000 2.000000 4
## [4448] {propos,
## success} => {method} 0.1333333 0.8000000 2.181818 4
## [4449] {method,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [4450] {task,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [4451] {approach,
## success} => {task} 0.1000000 0.7500000 2.045455 3
## [4452] {task,
## success} => {data} 0.1000000 0.7500000 1.730769 3
## [4453] {data,
## success} => {task} 0.1000000 1.0000000 2.727273 3
## [4454] {task,
## success} => {learn} 0.1000000 0.7500000 1.730769 3
## [4455] {task,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [4456] {task,
## success} => {propos} 0.1000000 0.7500000 1.500000 3
## [4457] {task,
## success} => {featur} 0.1333333 1.0000000 1.875000 4
## [4458] {featur,
## success} => {task} 0.1333333 0.8000000 2.181818 4
## [4459] {approach,
## success} => {learn} 0.1000000 0.7500000 1.730769 3
## [4460] {approach,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [4461] {approach,
## success} => {propos} 0.1333333 1.0000000 2.000000 4
## [4462] {propos,
## success} => {approach} 0.1333333 0.8000000 2.000000 4
## [4463] {approach,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [4464] {perform,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [4465] {perform,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [4466] {data,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [4467] {data,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [4468] {learn,
## success} => {represent} 0.1666667 1.0000000 2.000000 5
## [4469] {represent,
## success} => {learn} 0.1666667 0.8333333 1.923077 5
## [4470] {learn,
## success} => {show} 0.1333333 0.8000000 1.500000 4
## [4471] {show,
## success} => {learn} 0.1333333 1.0000000 2.307692 4
## [4472] {learn,
## success} => {propos} 0.1333333 0.8000000 1.600000 4
## [4473] {propos,
## success} => {learn} 0.1333333 0.8000000 1.846154 4
## [4474] {model,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [4475] {learn,
## success} => {featur} 0.1333333 0.8000000 1.500000 4
## [4476] {featur,
## success} => {learn} 0.1333333 0.8000000 1.846154 4
## [4477] {show,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [4478] {represent,
## success} => {propos} 0.1666667 0.8333333 1.666667 5
## [4479] {propos,
## success} => {represent} 0.1666667 1.0000000 2.000000 5
## [4480] {model,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [4481] {represent,
## success} => {featur} 0.1666667 0.8333333 1.562500 5
## [4482] {featur,
## success} => {represent} 0.1666667 1.0000000 2.000000 5
## [4483] {show,
## success} => {propos} 0.1000000 0.7500000 1.500000 3
## [4484] {show,
## success} => {model} 0.1000000 0.7500000 1.406250 3
## [4485] {model,
## success} => {show} 0.1000000 1.0000000 1.875000 3
## [4486] {show,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [4487] {propos,
## success} => {featur} 0.1333333 0.8000000 1.500000 4
## [4488] {featur,
## success} => {propos} 0.1333333 0.8000000 1.600000 4
## [4489] {architectur,
## experi} => {classif} 0.1333333 1.0000000 3.750000 4
## [4490] {classif,
## experi} => {architectur} 0.1333333 1.0000000 3.750000 4
## [4491] {classif,
## architectur} => {experi} 0.1333333 1.0000000 3.750000 4
## [4492] {architectur,
## experi} => {neural} 0.1000000 0.7500000 2.250000 3
## [4493] {experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [4494] {architectur,
## neural} => {experi} 0.1000000 0.7500000 2.812500 3
## [4495] {architectur,
## experi} => {method} 0.1000000 0.7500000 2.045455 3
## [4496] {method,
## architectur} => {experi} 0.1000000 0.7500000 2.812500 3
## [4497] {architectur,
## experi} => {approach} 0.1000000 0.7500000 1.875000 3
## [4498] {approach,
## experi} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4499] {approach,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [4500] {architectur,
## experi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4501] {dataset,
## experi} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4502] {architectur,
## experi} => {propos} 0.1333333 1.0000000 2.000000 4
## [4503] {experi,
## propos} => {architectur} 0.1333333 0.8000000 3.000000 4
## [4504] {architectur,
## propos} => {experi} 0.1333333 0.8000000 3.000000 4
## [4505] {architectur,
## experi} => {featur} 0.1000000 0.7500000 1.406250 3
## [4506] {featur,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [4507] {architectur,
## experi} => {network} 0.1333333 1.0000000 1.578947 4
## [4508] {make,
## experi} => {paper} 0.1000000 1.0000000 3.000000 3
## [4509] {paper,
## experi} => {make} 0.1000000 0.7500000 2.500000 3
## [4510] {make,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [4511] {classif,
## experi} => {neural} 0.1000000 0.7500000 2.250000 3
## [4512] {experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [4513] {classif,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [4514] {classif,
## experi} => {method} 0.1000000 0.7500000 2.045455 3
## [4515] {classif,
## experi} => {approach} 0.1000000 0.7500000 1.875000 3
## [4516] {approach,
## experi} => {classif} 0.1000000 0.7500000 2.812500 3
## [4517] {classif,
## experi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4518] {dataset,
## experi} => {classif} 0.1000000 0.7500000 2.812500 3
## [4519] {classif,
## dataset} => {experi} 0.1000000 0.7500000 2.812500 3
## [4520] {classif,
## experi} => {propos} 0.1333333 1.0000000 2.000000 4
## [4521] {experi,
## propos} => {classif} 0.1333333 0.8000000 3.000000 4
## [4522] {classif,
## experi} => {featur} 0.1000000 0.7500000 1.406250 3
## [4523] {featur,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [4524] {classif,
## experi} => {network} 0.1333333 1.0000000 1.578947 4
## [4525] {classif,
## network} => {experi} 0.1333333 0.8000000 3.000000 4
## [4526] {paper,
## experi} => {result} 0.1000000 0.7500000 2.250000 3
## [4527] {experi,
## result} => {paper} 0.1000000 0.7500000 2.250000 3
## [4528] {paper,
## result} => {experi} 0.1000000 0.7500000 2.812500 3
## [4529] {paper,
## experi} => {method} 0.1000000 0.7500000 2.045455 3
## [4530] {method,
## paper} => {experi} 0.1000000 0.7500000 2.812500 3
## [4531] {experi,
## result} => {method} 0.1000000 0.7500000 2.045455 3
## [4532] {method,
## result} => {experi} 0.1000000 1.0000000 3.750000 3
## [4533] {experi,
## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4534] {algorithm,
## experi} => {result} 0.1000000 0.7500000 2.250000 3
## [4535] {experi,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [4536] {experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [4537] {method,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [4538] {experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [4539] {approach,
## experi} => {neural} 0.1000000 0.7500000 2.250000 3
## [4540] {experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [4541] {experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [4542] {algorithm,
## experi} => {method} 0.1000000 0.7500000 2.045455 3
## [4543] {method,
## algorithm} => {experi} 0.1000000 0.7500000 2.812500 3
## [4544] {method,
## experi} => {approach} 0.1333333 0.8000000 2.000000 4
## [4545] {approach,
## experi} => {method} 0.1333333 1.0000000 2.727273 4
## [4546] {method,
## experi} => {perform} 0.1333333 0.8000000 1.714286 4
## [4547] {experi,
## perform} => {method} 0.1333333 1.0000000 2.727273 4
## [4548] {dataset,
## experi} => {method} 0.1000000 0.7500000 2.045455 3
## [4549] {method,
## experi} => {show} 0.1333333 0.8000000 1.500000 4
## [4550] {show,
## experi} => {method} 0.1333333 1.0000000 2.727273 4
## [4551] {method,
## experi} => {propos} 0.1333333 0.8000000 1.600000 4
## [4552] {experi,
## propos} => {method} 0.1333333 0.8000000 2.181818 4
## [4553] {model,
## experi} => {method} 0.1000000 0.7500000 2.045455 3
## [4554] {algorithm,
## experi} => {perform} 0.1000000 0.7500000 1.607143 3
## [4555] {experi,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4556] {algorithm,
## experi} => {show} 0.1000000 0.7500000 1.406250 3
## [4557] {show,
## experi} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4558] {algorithm,
## experi} => {network} 0.1000000 0.7500000 1.184211 3
## [4559] {train,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [4560] {approach,
## experi} => {perform} 0.1000000 0.7500000 1.607143 3
## [4561] {experi,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [4562] {approach,
## experi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4563] {dataset,
## experi} => {approach} 0.1000000 0.7500000 1.875000 3
## [4564] {approach,
## experi} => {show} 0.1000000 0.7500000 1.406250 3
## [4565] {show,
## experi} => {approach} 0.1000000 0.7500000 1.875000 3
## [4566] {approach,
## experi} => {propos} 0.1333333 1.0000000 2.000000 4
## [4567] {experi,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [4568] {approach,
## experi} => {network} 0.1000000 0.7500000 1.184211 3
## [4569] {experi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [4570] {experi,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4571] {dataset,
## experi} => {perform} 0.1000000 0.7500000 1.607143 3
## [4572] {experi,
## perform} => {show} 0.1333333 1.0000000 1.875000 4
## [4573] {show,
## experi} => {perform} 0.1333333 1.0000000 2.142857 4
## [4574] {experi,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [4575] {experi,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [4576] {model,
## experi} => {perform} 0.1000000 0.7500000 1.607143 3
## [4577] {dataset,
## experi} => {show} 0.1000000 0.7500000 1.406250 3
## [4578] {show,
## experi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4579] {dataset,
## experi} => {propos} 0.1333333 1.0000000 2.000000 4
## [4580] {experi,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4581] {dataset,
## experi} => {model} 0.1000000 0.7500000 1.406250 3
## [4582] {model,
## experi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4583] {dataset,
## experi} => {network} 0.1000000 0.7500000 1.184211 3
## [4584] {experi,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [4585] {show,
## experi} => {propos} 0.1000000 0.7500000 1.500000 3
## [4586] {show,
## experi} => {model} 0.1000000 0.7500000 1.406250 3
## [4587] {model,
## experi} => {show} 0.1000000 0.7500000 1.406250 3
## [4588] {model,
## experi} => {propos} 0.1000000 0.7500000 1.500000 3
## [4589] {featur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [4590] {experi,
## propos} => {network} 0.1333333 0.8000000 1.263158 4
## [4591] {featur,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [4592] {classif,
## object} => {recognit} 0.1000000 0.7500000 2.500000 3
## [4593] {object,
## recognit} => {classif} 0.1000000 0.7500000 2.812500 3
## [4594] {classif,
## recognit} => {object} 0.1000000 1.0000000 3.750000 3
## [4595] {classif,
## object} => {perform} 0.1000000 0.7500000 1.607143 3
## [4596] {classif,
## object} => {learn} 0.1000000 0.7500000 1.730769 3
## [4597] {object,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [4598] {classif,
## object} => {show} 0.1000000 0.7500000 1.406250 3
## [4599] {classif,
## object} => {propos} 0.1333333 1.0000000 2.000000 4
## [4600] {classif,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [4601] {classif,
## object} => {featur} 0.1333333 1.0000000 1.875000 4
## [4602] {object,
## problem} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4603] {algorithm,
## object} => {problem} 0.1000000 0.7500000 2.500000 3
## [4604] {algorithm,
## problem} => {object} 0.1000000 0.7500000 2.812500 3
## [4605] {object,
## problem} => {perform} 0.1000000 0.7500000 1.607143 3
## [4606] {object,
## problem} => {show} 0.1000000 0.7500000 1.406250 3
## [4607] {object,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [4608] {object,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [4609] {object,
## recognit} => {perform} 0.1000000 0.7500000 1.607143 3
## [4610] {perform,
## recognit} => {object} 0.1000000 0.7500000 2.812500 3
## [4611] {object,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [4612] {object,
## recognit} => {propos} 0.1333333 1.0000000 2.000000 4
## [4613] {object,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [4614] {improv,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [4615] {method,
## object} => {show} 0.1333333 1.0000000 1.875000 4
## [4616] {method,
## object} => {propos} 0.1000000 0.7500000 1.500000 3
## [4617] {method,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [4618] {method,
## object} => {featur} 0.1000000 0.7500000 1.406250 3
## [4619] {algorithm,
## object} => {perform} 0.1000000 0.7500000 1.607143 3
## [4620] {algorithm,
## object} => {show} 0.1000000 0.7500000 1.406250 3
## [4621] {algorithm,
## object} => {propos} 0.1000000 0.7500000 1.500000 3
## [4622] {algorithm,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [4623] {algorithm,
## object} => {featur} 0.1000000 0.7500000 1.406250 3
## [4624] {task,
## object} => {data} 0.1333333 1.0000000 2.307692 4
## [4625] {data,
## object} => {task} 0.1333333 1.0000000 2.727273 4
## [4626] {task,
## object} => {learn} 0.1000000 0.7500000 1.730769 3
## [4627] {object,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [4628] {task,
## object} => {propos} 0.1333333 1.0000000 2.000000 4
## [4629] {task,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [4630] {task,
## object} => {featur} 0.1333333 1.0000000 1.875000 4
## [4631] {train,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [4632] {approach,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [4633] {approach,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [4634] {object,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [4635] {represent,
## object} => {perform} 0.1000000 0.7500000 1.607143 3
## [4636] {object,
## perform} => {show} 0.1333333 0.8000000 1.500000 4
## [4637] {object,
## perform} => {propos} 0.1666667 1.0000000 2.000000 5
## [4638] {object,
## propos} => {perform} 0.1666667 0.7142857 1.530612 5
## [4639] {object,
## perform} => {featur} 0.1333333 0.8000000 1.500000 4
## [4640] {data,
## object} => {learn} 0.1000000 0.7500000 1.730769 3
## [4641] {object,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [4642] {data,
## object} => {propos} 0.1333333 1.0000000 2.000000 4
## [4643] {data,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [4644] {data,
## object} => {featur} 0.1333333 1.0000000 1.875000 4
## [4645] {dataset,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [4646] {dataset,
## object} => {model} 0.1000000 1.0000000 1.875000 3
## [4647] {dataset,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [4648] {dataset,
## object} => {network} 0.1000000 1.0000000 1.578947 3
## [4649] {network,
## object} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4650] {object,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [4651] {object,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [4652] {object,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [4653] {object,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [4654] {represent,
## object} => {show} 0.1000000 0.7500000 1.406250 3
## [4655] {represent,
## object} => {propos} 0.1333333 1.0000000 2.000000 4
## [4656] {represent,
## object} => {featur} 0.1000000 0.7500000 1.406250 3
## [4657] {show,
## object} => {propos} 0.1666667 0.8333333 1.666667 5
## [4658] {object,
## propos} => {show} 0.1666667 0.7142857 1.339286 5
## [4659] {model,
## object} => {show} 0.1333333 0.8000000 1.500000 4
## [4660] {model,
## object} => {propos} 0.1333333 0.8000000 1.600000 4
## [4661] {object,
## propos} => {featur} 0.2000000 0.8571429 1.607143 6
## [4662] {featur,
## object} => {propos} 0.2000000 1.0000000 2.000000 6
## [4663] {network,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [4664] {model,
## object} => {featur} 0.1333333 0.8000000 1.500000 4
## [4665] {network,
## object} => {model} 0.1000000 1.0000000 1.875000 3
## [4666] {network,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [4667] {classif,
## architectur} => {neural} 0.1000000 0.7500000 2.250000 3
## [4668] {architectur,
## neural} => {classif} 0.1000000 0.7500000 2.812500 3
## [4669] {classif,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [4670] {classif,
## architectur} => {method} 0.1000000 0.7500000 2.045455 3
## [4671] {method,
## architectur} => {classif} 0.1000000 0.7500000 2.812500 3
## [4672] {classif,
## architectur} => {approach} 0.1000000 0.7500000 1.875000 3
## [4673] {approach,
## architectur} => {classif} 0.1000000 1.0000000 3.750000 3
## [4674] {classif,
## architectur} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4675] {classif,
## dataset} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4676] {classif,
## architectur} => {propos} 0.1333333 1.0000000 2.000000 4
## [4677] {architectur,
## propos} => {classif} 0.1333333 0.8000000 3.000000 4
## [4678] {classif,
## architectur} => {featur} 0.1000000 0.7500000 1.406250 3
## [4679] {classif,
## architectur} => {network} 0.1333333 1.0000000 1.578947 4
## [4680] {classif,
## network} => {architectur} 0.1333333 0.8000000 3.000000 4
## [4681] {architectur,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [4682] {model,
## architectur} => {recognit} 0.1000000 0.7500000 2.500000 3
## [4683] {model,
## recognit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4684] {architectur,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [4685] {architectur,
## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [4686] {architectur,
## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [4687] {architectur,
## improv} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4688] {algorithm,
## architectur} => {improv} 0.1000000 0.7500000 2.500000 3
## [4689] {architectur,
## improv} => {work} 0.1000000 0.7500000 1.875000 3
## [4690] {improv,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4691] {architectur,
## improv} => {perform} 0.1333333 1.0000000 2.142857 4
## [4692] {architectur,
## perform} => {improv} 0.1333333 0.8000000 2.666667 4
## [4693] {architectur,
## improv} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4694] {architectur,
## improv} => {network} 0.1333333 1.0000000 1.578947 4
## [4695] {network,
## improv} => {architectur} 0.1333333 0.8000000 3.000000 4
## [4696] {architectur,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [4697] {architectur,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [4698] {architectur,
## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4699] {algorithm,
## architectur} => {result} 0.1000000 0.7500000 2.250000 3
## [4700] {architectur,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [4701] {represent,
## architectur} => {result} 0.1000000 1.0000000 3.000000 3
## [4702] {architectur,
## result} => {featur} 0.1333333 1.0000000 1.875000 4
## [4703] {architectur,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [4704] {architectur,
## neural} => {method} 0.1000000 0.7500000 2.045455 3
## [4705] {method,
## architectur} => {neural} 0.1000000 0.7500000 2.250000 3
## [4706] {method,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [4707] {architectur,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4708] {algorithm,
## architectur} => {neural} 0.1000000 0.7500000 2.250000 3
## [4709] {architectur,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [4710] {train,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [4711] {architectur,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [4712] {approach,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [4713] {architectur,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [4714] {neural,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4715] {architectur,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4716] {architectur,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [4717] {architectur,
## neural} => {featur} 0.1000000 0.7500000 1.406250 3
## [4718] {architectur,
## neural} => {network} 0.1333333 1.0000000 1.578947 4
## [4719] {method,
## architectur} => {approach} 0.1000000 0.7500000 1.875000 3
## [4720] {approach,
## architectur} => {method} 0.1000000 1.0000000 2.727273 3
## [4721] {method,
## architectur} => {perform} 0.1000000 0.7500000 1.607143 3
## [4722] {method,
## architectur} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4723] {method,
## architectur} => {propos} 0.1333333 1.0000000 2.000000 4
## [4724] {architectur,
## propos} => {method} 0.1333333 0.8000000 2.181818 4
## [4725] {method,
## architectur} => {featur} 0.1000000 0.7500000 1.406250 3
## [4726] {method,
## architectur} => {network} 0.1333333 1.0000000 1.578947 4
## [4727] {algorithm,
## architectur} => {perform} 0.1000000 0.7500000 1.607143 3
## [4728] {algorithm,
## architectur} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4729] {algorithm,
## dataset} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4730] {algorithm,
## architectur} => {show} 0.1000000 0.7500000 1.406250 3
## [4731] {show,
## architectur} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [4732] {algorithm,
## architectur} => {featur} 0.1000000 0.7500000 1.406250 3
## [4733] {algorithm,
## architectur} => {network} 0.1000000 0.7500000 1.184211 3
## [4734] {task,
## architectur} => {learn} 0.1000000 1.0000000 2.307692 3
## [4735] {architectur,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [4736] {task,
## architectur} => {featur} 0.1000000 1.0000000 1.875000 3
## [4737] {train,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [4738] {approach,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [4739] {approach,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [4740] {architectur,
## work} => {perform} 0.1333333 0.8000000 1.714286 4
## [4741] {architectur,
## perform} => {work} 0.1333333 0.8000000 2.000000 4
## [4742] {architectur,
## work} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4743] {architectur,
## dataset} => {work} 0.1333333 0.8000000 2.000000 4
## [4744] {architectur,
## work} => {network} 0.1666667 1.0000000 1.578947 5
## [4745] {network,
## architectur} => {work} 0.1666667 0.7142857 1.785714 5
## [4746] {architectur,
## perform} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4747] {architectur,
## dataset} => {perform} 0.1333333 0.8000000 1.714286 4
## [4748] {architectur,
## perform} => {network} 0.1666667 1.0000000 1.578947 5
## [4749] {network,
## architectur} => {perform} 0.1666667 0.7142857 1.530612 5
## [4750] {network,
## perform} => {architectur} 0.1666667 1.0000000 3.750000 5
## [4751] {data,
## architectur} => {featur} 0.1000000 1.0000000 1.875000 3
## [4752] {architectur,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [4753] {architectur,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4754] {architectur,
## dataset} => {featur} 0.1333333 0.8000000 1.500000 4
## [4755] {architectur,
## dataset} => {network} 0.1666667 1.0000000 1.578947 5
## [4756] {network,
## architectur} => {dataset} 0.1666667 0.7142857 1.648352 5
## [4757] {architectur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [4758] {represent,
## architectur} => {featur} 0.1000000 1.0000000 1.875000 3
## [4759] {architectur,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [4760] {architectur,
## propos} => {network} 0.1666667 1.0000000 1.578947 5
## [4761] {network,
## architectur} => {propos} 0.1666667 0.7142857 1.428571 5
## [4762] {model,
## architectur} => {featur} 0.1000000 0.7500000 1.406250 3
## [4763] {model,
## architectur} => {network} 0.1000000 0.7500000 1.184211 3
## [4764] {featur,
## architectur} => {network} 0.1666667 0.8333333 1.315789 5
## [4765] {network,
## architectur} => {featur} 0.1666667 0.7142857 1.339286 5
## [4766] {classif,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [4767] {classif,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [4768] {classif,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [4769] {make,
## problem} => {approach} 0.1333333 0.8000000 2.000000 4
## [4770] {approach,
## problem} => {make} 0.1333333 0.8000000 2.666667 4
## [4771] {make,
## problem} => {perform} 0.1666667 1.0000000 2.142857 5
## [4772] {make,
## perform} => {problem} 0.1666667 0.7142857 2.380952 5
## [4773] {make,
## dataset} => {problem} 0.1000000 0.7500000 2.500000 3
## [4774] {dataset,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [4775] {represent,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [4776] {make,
## problem} => {model} 0.1333333 0.8000000 1.500000 4
## [4777] {make,
## paper} => {method} 0.1333333 0.8000000 2.181818 4
## [4778] {make,
## method} => {paper} 0.1333333 0.8000000 2.400000 4
## [4779] {method,
## paper} => {make} 0.1333333 1.0000000 3.333333 4
## [4780] {make,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [4781] {approach,
## paper} => {make} 0.1000000 1.0000000 3.333333 3
## [4782] {paper,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [4783] {make,
## paper} => {represent} 0.1333333 0.8000000 1.600000 4
## [4784] {make,
## show} => {paper} 0.1000000 0.7500000 2.250000 3
## [4785] {paper,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [4786] {make,
## paper} => {model} 0.1333333 0.8000000 1.500000 4
## [4787] {make,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [4788] {make,
## improv} => {model} 0.1000000 1.0000000 1.875000 3
## [4789] {make,
## method} => {approach} 0.1333333 0.8000000 2.000000 4
## [4790] {make,
## method} => {show} 0.1333333 0.8000000 1.500000 4
## [4791] {make,
## show} => {method} 0.1333333 1.0000000 2.727273 4
## [4792] {make,
## method} => {model} 0.1333333 0.8000000 1.500000 4
## [4793] {make,
## task} => {approach} 0.1333333 1.0000000 2.500000 4
## [4794] {make,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [4795] {make,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [4796] {make,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [4797] {make,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [4798] {make,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [4799] {make,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [4800] {make,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [4801] {make,
## learn} => {approach} 0.1333333 0.8000000 2.000000 4
## [4802] {approach,
## make} => {represent} 0.1666667 0.8333333 1.666667 5
## [4803] {make,
## represent} => {approach} 0.1666667 0.8333333 2.083333 5
## [4804] {make,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [4805] {make,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [4806] {approach,
## make} => {featur} 0.1666667 0.8333333 1.562500 5
## [4807] {featur,
## make} => {approach} 0.1666667 0.8333333 2.083333 5
## [4808] {data,
## make} => {perform} 0.1333333 0.8000000 1.714286 4
## [4809] {make,
## dataset} => {perform} 0.1333333 1.0000000 2.142857 4
## [4810] {make,
## learn} => {perform} 0.1333333 0.8000000 1.714286 4
## [4811] {make,
## show} => {perform} 0.1000000 0.7500000 1.607143 3
## [4812] {make,
## propos} => {perform} 0.1333333 0.8000000 1.714286 4
## [4813] {make,
## perform} => {model} 0.2000000 0.8571429 1.607143 6
## [4814] {make,
## model} => {perform} 0.2000000 0.8571429 1.836735 6
## [4815] {model,
## perform} => {make} 0.2000000 0.7500000 2.500000 6
## [4816] {data,
## make} => {represent} 0.1333333 0.8000000 1.600000 4
## [4817] {data,
## make} => {model} 0.1333333 0.8000000 1.500000 4
## [4818] {data,
## make} => {featur} 0.1333333 0.8000000 1.500000 4
## [4819] {make,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [4820] {make,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4821] {make,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [4822] {make,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [4823] {make,
## dataset} => {model} 0.1333333 1.0000000 1.875000 4
## [4824] {make,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [4825] {make,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [4826] {make,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [4827] {make,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [4828] {make,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [4829] {make,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [4830] {make,
## represent} => {propos} 0.1666667 0.8333333 1.666667 5
## [4831] {make,
## propos} => {represent} 0.1666667 1.0000000 2.000000 5
## [4832] {make,
## represent} => {featur} 0.1666667 0.8333333 1.562500 5
## [4833] {featur,
## make} => {represent} 0.1666667 0.8333333 1.666667 5
## [4834] {make,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [4835] {make,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [4836] {classif,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [4837] {classif,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [4838] {classif,
## perform} => {problem} 0.1333333 0.8000000 2.666667 4
## [4839] {classif,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [4840] {classif,
## problem} => {show} 0.1333333 1.0000000 1.875000 4
## [4841] {classif,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [4842] {classif,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [4843] {classif,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [4844] {classif,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [4845] {classif,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [4846] {classif,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [4847] {classif,
## paper} => {featur} 0.1000000 1.0000000 1.875000 3
## [4848] {classif,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [4849] {classif,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [4850] {classif,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [4851] {method,
## improv} => {classif} 0.1000000 0.7500000 2.812500 3
## [4852] {classif,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [4853] {approach,
## improv} => {classif} 0.1000000 0.7500000 2.812500 3
## [4854] {classif,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [4855] {classif,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4856] {classif,
## dataset} => {improv} 0.1000000 0.7500000 2.500000 3
## [4857] {classif,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [4858] {classif,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [4859] {method,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [4860] {classif,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [4861] {classif,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [4862] {classif,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [4863] {classif,
## train} => {method} 0.1000000 0.7500000 2.045455 3
## [4864] {method,
## train} => {classif} 0.1000000 0.7500000 2.812500 3
## [4865] {classif,
## method} => {approach} 0.1666667 0.8333333 2.083333 5
## [4866] {approach,
## classif} => {method} 0.1666667 1.0000000 2.727273 5
## [4867] {approach,
## method} => {classif} 0.1666667 0.7142857 2.678571 5
## [4868] {classif,
## perform} => {method} 0.1333333 0.8000000 2.181818 4
## [4869] {classif,
## dataset} => {method} 0.1000000 0.7500000 2.045455 3
## [4870] {classif,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [4871] {classif,
## method} => {show} 0.1666667 0.8333333 1.562500 5
## [4872] {classif,
## show} => {method} 0.1666667 0.8333333 2.272727 5
## [4873] {classif,
## method} => {featur} 0.1666667 0.8333333 1.562500 5
## [4874] {classif,
## featur} => {method} 0.1666667 0.7142857 1.948052 5
## [4875] {featur,
## method} => {classif} 0.1666667 0.7142857 2.678571 5
## [4876] {classif,
## network} => {method} 0.1333333 0.8000000 2.181818 4
## [4877] {classif,
## algorithm} => {perform} 0.1000000 1.0000000 2.142857 3
## [4878] {classif,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [4879] {classif,
## algorithm} => {propos} 0.1000000 1.0000000 2.000000 3
## [4880] {classif,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [4881] {classif,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [4882] {classif,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [4883] {classif,
## data} => {task} 0.1000000 1.0000000 2.727273 3
## [4884] {classif,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [4885] {classif,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [4886] {classif,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [4887] {classif,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [4888] {classif,
## task} => {network} 0.1000000 0.7500000 1.184211 3
## [4889] {classif,
## train} => {approach} 0.1000000 0.7500000 1.875000 3
## [4890] {classif,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [4891] {classif,
## train} => {propos} 0.1000000 0.7500000 1.500000 3
## [4892] {classif,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [4893] {classif,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [4894] {classif,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [4895] {approach,
## classif} => {show} 0.1333333 0.8000000 1.500000 4
## [4896] {approach,
## classif} => {featur} 0.1333333 0.8000000 1.500000 4
## [4897] {approach,
## classif} => {network} 0.1333333 0.8000000 1.263158 4
## [4898] {classif,
## network} => {approach} 0.1333333 0.8000000 2.000000 4
## [4899] {classif,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [4900] {classif,
## perform} => {show} 0.1666667 1.0000000 1.875000 5
## [4901] {classif,
## show} => {perform} 0.1666667 0.8333333 1.785714 5
## [4902] {classif,
## perform} => {propos} 0.1333333 0.8000000 1.600000 4
## [4903] {classif,
## perform} => {featur} 0.1333333 0.8000000 1.500000 4
## [4904] {classif,
## data} => {model} 0.1000000 1.0000000 1.875000 3
## [4905] {classif,
## data} => {featur} 0.1000000 1.0000000 1.875000 3
## [4906] {classif,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [4907] {classif,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [4908] {classif,
## dataset} => {model} 0.1000000 0.7500000 1.406250 3
## [4909] {classif,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [4910] {classif,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [4911] {classif,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [4912] {classif,
## learn} => {featur} 0.1666667 1.0000000 1.875000 5
## [4913] {classif,
## featur} => {learn} 0.1666667 0.7142857 1.648352 5
## [4914] {classif,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [4915] {classif,
## model} => {show} 0.1333333 0.8000000 1.500000 4
## [4916] {classif,
## show} => {featur} 0.1666667 0.8333333 1.562500 5
## [4917] {classif,
## featur} => {show} 0.1666667 0.7142857 1.339286 5
## [4918] {classif,
## propos} => {featur} 0.1666667 0.8333333 1.562500 5
## [4919] {classif,
## featur} => {propos} 0.1666667 0.7142857 1.428571 5
## [4920] {classif,
## network} => {propos} 0.1333333 0.8000000 1.600000 4
## [4921] {classif,
## model} => {featur} 0.1666667 1.0000000 1.875000 5
## [4922] {classif,
## featur} => {model} 0.1666667 0.7142857 1.339286 5
## [4923] {classif,
## network} => {featur} 0.1333333 0.8000000 1.500000 4
## [4924] {improv,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [4925] {method,
## improv} => {problem} 0.1000000 0.7500000 2.500000 3
## [4926] {improv,
## problem} => {perform} 0.1000000 0.7500000 1.607143 3
## [4927] {improv,
## problem} => {show} 0.1000000 0.7500000 1.406250 3
## [4928] {improv,
## problem} => {model} 0.1000000 0.7500000 1.406250 3
## [4929] {method,
## problem} => {perform} 0.1333333 0.8000000 1.714286 4
## [4930] {dataset,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [4931] {method,
## problem} => {show} 0.1666667 1.0000000 1.875000 5
## [4932] {show,
## problem} => {method} 0.1666667 0.8333333 2.272727 5
## [4933] {algorithm,
## problem} => {train} 0.1000000 0.7500000 1.875000 3
## [4934] {train,
## problem} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [4935] {algorithm,
## problem} => {perform} 0.1000000 0.7500000 1.607143 3
## [4936] {algorithm,
## problem} => {show} 0.1000000 0.7500000 1.406250 3
## [4937] {algorithm,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [4938] {task,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [4939] {task,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [4940] {task,
## problem} => {data} 0.1000000 1.0000000 2.307692 3
## [4941] {data,
## problem} => {task} 0.1000000 1.0000000 2.727273 3
## [4942] {task,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [4943] {task,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [4944] {train,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [4945] {approach,
## problem} => {perform} 0.1666667 1.0000000 2.142857 5
## [4946] {approach,
## perform} => {problem} 0.1666667 0.8333333 2.777778 5
## [4947] {approach,
## problem} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4948] {dataset,
## problem} => {approach} 0.1333333 1.0000000 2.500000 4
## [4949] {represent,
## problem} => {approach} 0.1000000 0.7500000 1.875000 3
## [4950] {approach,
## problem} => {propos} 0.1333333 0.8000000 1.600000 4
## [4951] {problem,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [4952] {data,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [4953] {dataset,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [4954] {problem,
## learn} => {perform} 0.1666667 1.0000000 2.142857 5
## [4955] {perform,
## learn} => {problem} 0.1666667 0.8333333 2.777778 5
## [4956] {represent,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [4957] {show,
## problem} => {perform} 0.1666667 0.8333333 1.785714 5
## [4958] {perform,
## problem} => {propos} 0.2000000 0.7500000 1.500000 6
## [4959] {propos,
## problem} => {perform} 0.2000000 1.0000000 2.142857 6
## [4960] {model,
## problem} => {perform} 0.1666667 0.8333333 1.785714 5
## [4961] {featur,
## problem} => {perform} 0.1666667 1.0000000 2.142857 5
## [4962] {data,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [4963] {data,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [4964] {dataset,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [4965] {dataset,
## problem} => {show} 0.1000000 0.7500000 1.406250 3
## [4966] {dataset,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [4967] {dataset,
## problem} => {model} 0.1000000 0.7500000 1.406250 3
## [4968] {represent,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [4969] {problem,
## learn} => {show} 0.1333333 0.8000000 1.500000 4
## [4970] {problem,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [4971] {problem,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [4972] {problem,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [4973] {featur,
## problem} => {learn} 0.1333333 0.8000000 1.846154 4
## [4974] {represent,
## problem} => {propos} 0.1333333 1.0000000 2.000000 4
## [4975] {represent,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [4976] {featur,
## problem} => {propos} 0.1333333 0.8000000 1.600000 4
## [4977] {paper,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [4978] {paper,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [4979] {paper,
## neural} => {represent} 0.1000000 1.0000000 2.000000 3
## [4980] {method,
## paper} => {approach} 0.1000000 0.7500000 1.875000 3
## [4981] {approach,
## paper} => {method} 0.1000000 1.0000000 2.727273 3
## [4982] {method,
## paper} => {represent} 0.1000000 0.7500000 1.500000 3
## [4983] {method,
## paper} => {show} 0.1000000 0.7500000 1.406250 3
## [4984] {method,
## paper} => {model} 0.1000000 0.7500000 1.406250 3
## [4985] {paper,
## task} => {train} 0.1333333 0.8000000 2.000000 4
## [4986] {task,
## train} => {paper} 0.1333333 0.8000000 2.400000 4
## [4987] {paper,
## task} => {data} 0.1333333 0.8000000 1.846154 4
## [4988] {paper,
## task} => {featur} 0.1333333 0.8000000 1.500000 4
## [4989] {featur,
## paper} => {task} 0.1333333 0.8000000 2.181818 4
## [4990] {paper,
## task} => {network} 0.1333333 0.8000000 1.263158 4
## [4991] {paper,
## learn} => {train} 0.1333333 0.8000000 2.000000 4
## [4992] {train,
## learn} => {paper} 0.1333333 0.8000000 2.400000 4
## [4993] {paper,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [4994] {paper,
## train} => {featur} 0.1666667 0.8333333 1.562500 5
## [4995] {featur,
## paper} => {train} 0.1666667 1.0000000 2.500000 5
## [4996] {featur,
## train} => {paper} 0.1666667 0.8333333 2.500000 5
## [4997] {approach,
## paper} => {represent} 0.1000000 1.0000000 2.000000 3
## [4998] {paper,
## perform} => {data} 0.1000000 0.7500000 1.730769 3
## [4999] {paper,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [5000] {paper,
## perform} => {show} 0.1000000 0.7500000 1.406250 3
## [5001] {paper,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [5002] {paper,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [5003] {paper,
## perform} => {model} 0.1333333 1.0000000 1.875000 4
## [5004] {featur,
## paper} => {data} 0.1333333 0.8000000 1.846154 4
## [5005] {paper,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [5006] {paper,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [5007] {paper,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [5008] {paper,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [5009] {paper,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [5010] {paper,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [5011] {featur,
## paper} => {learn} 0.1333333 0.8000000 1.846154 4
## [5012] {paper,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [5013] {featur,
## paper} => {represent} 0.1333333 0.8000000 1.600000 4
## [5014] {paper,
## show} => {model} 0.1666667 0.8333333 1.562500 5
## [5015] {model,
## paper} => {show} 0.1666667 0.8333333 1.562500 5
## [5016] {paper,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [5017] {paper,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [5018] {improv,
## recognit} => {perform} 0.1000000 1.0000000 2.142857 3
## [5019] {perform,
## recognit} => {improv} 0.1000000 0.7500000 2.500000 3
## [5020] {improv,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [5021] {recognit,
## result} => {task} 0.1000000 0.7500000 2.045455 3
## [5022] {task,
## recognit} => {result} 0.1000000 0.7500000 2.250000 3
## [5023] {recognit,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [5024] {recognit,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [5025] {recognit,
## result} => {learn} 0.1000000 0.7500000 1.730769 3
## [5026] {recognit,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [5027] {recognit,
## result} => {show} 0.1333333 1.0000000 1.875000 4
## [5028] {show,
## result} => {recognit} 0.1333333 0.8000000 2.666667 4
## [5029] {recognit,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [5030] {recognit,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [5031] {neural,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [5032] {neural,
## recognit} => {propos} 0.1000000 0.7500000 1.500000 3
## [5033] {neural,
## recognit} => {network} 0.1000000 0.7500000 1.184211 3
## [5034] {task,
## recognit} => {data} 0.1333333 1.0000000 2.307692 4
## [5035] {data,
## recognit} => {task} 0.1333333 0.8000000 2.181818 4
## [5036] {task,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [5037] {task,
## recognit} => {learn} 0.1333333 1.0000000 2.307692 4
## [5038] {task,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [5039] {task,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [5040] {task,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [5041] {task,
## recognit} => {network} 0.1000000 0.7500000 1.184211 3
## [5042] {train,
## recognit} => {data} 0.1000000 0.7500000 1.730769 3
## [5043] {train,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [5044] {train,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [5045] {train,
## recognit} => {represent} 0.1333333 1.0000000 2.000000 4
## [5046] {train,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [5047] {train,
## recognit} => {propos} 0.1000000 0.7500000 1.500000 3
## [5048] {recognit,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [5049] {recognit,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [5050] {perform,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [5051] {perform,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [5052] {perform,
## recognit} => {propos} 0.1333333 1.0000000 2.000000 4
## [5053] {perform,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [5054] {data,
## recognit} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5055] {dataset,
## recognit} => {data} 0.1333333 0.8000000 1.846154 4
## [5056] {data,
## recognit} => {learn} 0.1666667 1.0000000 2.307692 5
## [5057] {recognit,
## learn} => {data} 0.1666667 0.8333333 1.923077 5
## [5058] {data,
## recognit} => {represent} 0.1333333 0.8000000 1.600000 4
## [5059] {model,
## recognit} => {data} 0.1000000 0.7500000 1.730769 3
## [5060] {data,
## recognit} => {featur} 0.1333333 0.8000000 1.500000 4
## [5061] {dataset,
## recognit} => {learn} 0.1333333 0.8000000 1.846154 4
## [5062] {dataset,
## recognit} => {propos} 0.1333333 0.8000000 1.600000 4
## [5063] {model,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [5064] {dataset,
## recognit} => {featur} 0.1333333 0.8000000 1.500000 4
## [5065] {dataset,
## recognit} => {network} 0.1333333 0.8000000 1.263158 4
## [5066] {network,
## recognit} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5067] {recognit,
## learn} => {represent} 0.1666667 0.8333333 1.666667 5
## [5068] {represent,
## recognit} => {learn} 0.1666667 0.8333333 1.923077 5
## [5069] {model,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [5070] {recognit,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [5071] {featur,
## recognit} => {learn} 0.1666667 0.8333333 1.923077 5
## [5072] {represent,
## recognit} => {show} 0.1666667 0.8333333 1.562500 5
## [5073] {show,
## recognit} => {represent} 0.1666667 0.8333333 1.666667 5
## [5074] {model,
## recognit} => {propos} 0.1000000 0.7500000 1.500000 3
## [5075] {model,
## recognit} => {featur} 0.1333333 1.0000000 1.875000 4
## [5076] {improv,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [5077] {improv,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [5078] {improv,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5079] {improv,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [5080] {improv,
## neural} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [5081] {algorithm,
## improv} => {neural} 0.1333333 0.8000000 2.400000 4
## [5082] {algorithm,
## neural} => {improv} 0.1333333 0.8000000 2.666667 4
## [5083] {improv,
## neural} => {train} 0.1333333 0.8000000 2.000000 4
## [5084] {approach,
## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [5085] {improv,
## neural} => {perform} 0.1333333 0.8000000 1.714286 4
## [5086] {neural,
## perform} => {improv} 0.1333333 1.0000000 3.333333 4
## [5087] {improv,
## neural} => {dataset} 0.1666667 1.0000000 2.307692 5
## [5088] {dataset,
## improv} => {neural} 0.1666667 0.8333333 2.500000 5
## [5089] {dataset,
## neural} => {improv} 0.1666667 0.8333333 2.777778 5
## [5090] {improv,
## neural} => {propos} 0.1333333 0.8000000 1.600000 4
## [5091] {improv,
## propos} => {neural} 0.1333333 0.8000000 2.400000 4
## [5092] {featur,
## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [5093] {improv,
## neural} => {network} 0.1333333 0.8000000 1.263158 4
## [5094] {network,
## improv} => {neural} 0.1333333 0.8000000 2.400000 4
## [5095] {method,
## improv} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [5096] {method,
## algorithm} => {improv} 0.1000000 0.7500000 2.500000 3
## [5097] {method,
## improv} => {approach} 0.1000000 0.7500000 1.875000 3
## [5098] {approach,
## improv} => {method} 0.1000000 0.7500000 2.045455 3
## [5099] {method,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [5100] {method,
## improv} => {dataset} 0.1000000 0.7500000 1.730769 3
## [5101] {method,
## improv} => {show} 0.1333333 1.0000000 1.875000 4
## [5102] {show,
## improv} => {method} 0.1333333 0.8000000 2.181818 4
## [5103] {method,
## improv} => {model} 0.1000000 0.7500000 1.406250 3
## [5104] {algorithm,
## improv} => {train} 0.1333333 0.8000000 2.000000 4
## [5105] {approach,
## improv} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [5106] {algorithm,
## improv} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5107] {algorithm,
## dataset} => {improv} 0.1333333 1.0000000 3.333333 4
## [5108] {algorithm,
## improv} => {network} 0.1333333 0.8000000 1.263158 4
## [5109] {network,
## improv} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [5110] {improv,
## work} => {train} 0.1000000 0.7500000 1.875000 3
## [5111] {train,
## perform} => {improv} 0.1333333 0.8000000 2.666667 4
## [5112] {represent,
## improv} => {train} 0.1000000 1.0000000 2.500000 3
## [5113] {improv,
## propos} => {train} 0.1333333 0.8000000 2.000000 4
## [5114] {approach,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [5115] {approach,
## improv} => {dataset} 0.1333333 1.0000000 2.307692 4
## [5116] {approach,
## improv} => {show} 0.1000000 0.7500000 1.406250 3
## [5117] {approach,
## improv} => {propos} 0.1000000 0.7500000 1.500000 3
## [5118] {approach,
## improv} => {model} 0.1000000 0.7500000 1.406250 3
## [5119] {approach,
## improv} => {network} 0.1000000 0.7500000 1.184211 3
## [5120] {improv,
## work} => {perform} 0.1333333 1.0000000 2.142857 4
## [5121] {improv,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [5122] {improv,
## perform} => {dataset} 0.1666667 0.7142857 1.648352 5
## [5123] {dataset,
## improv} => {perform} 0.1666667 0.8333333 1.785714 5
## [5124] {represent,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [5125] {show,
## improv} => {perform} 0.1333333 0.8000000 1.714286 4
## [5126] {improv,
## propos} => {perform} 0.1333333 0.8000000 1.714286 4
## [5127] {featur,
## improv} => {perform} 0.1333333 1.0000000 2.142857 4
## [5128] {network,
## improv} => {perform} 0.1333333 0.8000000 1.714286 4
## [5129] {network,
## perform} => {improv} 0.1333333 0.8000000 2.666667 4
## [5130] {improv,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5131] {featur,
## improv} => {dataset} 0.1333333 1.0000000 2.307692 4
## [5132] {network,
## improv} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5133] {featur,
## improv} => {model} 0.1000000 0.7500000 1.406250 3
## [5134] {neural,
## result} => {train} 0.1333333 0.8000000 2.000000 4
## [5135] {neural,
## result} => {approach} 0.1333333 0.8000000 2.000000 4
## [5136] {approach,
## result} => {neural} 0.1333333 0.8000000 2.400000 4
## [5137] {neural,
## result} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5138] {neural,
## result} => {propos} 0.1333333 0.8000000 1.600000 4
## [5139] {propos,
## result} => {neural} 0.1333333 0.8000000 2.400000 4
## [5140] {neural,
## result} => {network} 0.1666667 1.0000000 1.578947 5
## [5141] {network,
## result} => {neural} 0.1666667 0.7142857 2.142857 5
## [5142] {perform,
## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [5143] {algorithm,
## dataset} => {result} 0.1000000 0.7500000 2.250000 3
## [5144] {model,
## result} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [5145] {task,
## result} => {data} 0.1333333 0.8000000 1.846154 4
## [5146] {task,
## result} => {learn} 0.1666667 1.0000000 2.307692 5
## [5147] {result,
## learn} => {task} 0.1666667 1.0000000 2.727273 5
## [5148] {task,
## result} => {represent} 0.1666667 1.0000000 2.000000 5
## [5149] {represent,
## result} => {task} 0.1666667 0.8333333 2.272727 5
## [5150] {task,
## result} => {featur} 0.1333333 0.8000000 1.500000 4
## [5151] {result,
## work} => {train} 0.1000000 0.7500000 1.875000 3
## [5152] {train,
## result} => {network} 0.2000000 1.0000000 1.578947 6
## [5153] {network,
## result} => {train} 0.2000000 0.8571429 2.142857 6
## [5154] {network,
## train} => {result} 0.2000000 0.7500000 2.250000 6
## [5155] {approach,
## result} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5156] {approach,
## result} => {propos} 0.1666667 1.0000000 2.000000 5
## [5157] {propos,
## result} => {approach} 0.1666667 1.0000000 2.500000 5
## [5158] {approach,
## result} => {network} 0.1333333 0.8000000 1.263158 4
## [5159] {result,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [5160] {result,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [5161] {result,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [5162] {result,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [5163] {perform,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [5164] {perform,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [5165] {perform,
## result} => {model} 0.1000000 0.7500000 1.406250 3
## [5166] {perform,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [5167] {result,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [5168] {data,
## result} => {represent} 0.1666667 0.8333333 1.666667 5
## [5169] {represent,
## result} => {data} 0.1666667 0.8333333 1.923077 5
## [5170] {show,
## result} => {data} 0.1333333 0.8000000 1.846154 4
## [5171] {propos,
## result} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5172] {dataset,
## result} => {network} 0.1666667 0.8333333 1.315789 5
## [5173] {network,
## result} => {dataset} 0.1666667 0.7142857 1.648352 5
## [5174] {result,
## learn} => {represent} 0.1666667 1.0000000 2.000000 5
## [5175] {represent,
## result} => {learn} 0.1666667 0.8333333 1.923077 5
## [5176] {result,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [5177] {represent,
## result} => {featur} 0.1666667 0.8333333 1.562500 5
## [5178] {featur,
## result} => {represent} 0.1666667 0.8333333 1.666667 5
## [5179] {propos,
## result} => {network} 0.1333333 0.8000000 1.263158 4
## [5180] {method,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [5181] {method,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [5182] {method,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [5183] {algorithm,
## neural} => {approach} 0.1333333 0.8000000 2.000000 4
## [5184] {approach,
## algorithm} => {neural} 0.1333333 0.8000000 2.400000 4
## [5185] {algorithm,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [5186] {neural,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [5187] {algorithm,
## neural} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5188] {algorithm,
## dataset} => {neural} 0.1333333 1.0000000 3.000000 4
## [5189] {algorithm,
## neural} => {network} 0.1666667 1.0000000 1.578947 5
## [5190] {network,
## algorithm} => {neural} 0.1666667 0.7142857 2.142857 5
## [5191] {approach,
## train} => {neural} 0.1333333 0.8000000 2.400000 4
## [5192] {neural,
## perform} => {train} 0.1000000 0.7500000 1.875000 3
## [5193] {data,
## neural} => {train} 0.1000000 1.0000000 2.500000 3
## [5194] {train,
## neural} => {dataset} 0.1666667 0.8333333 1.923077 5
## [5195] {dataset,
## neural} => {train} 0.1666667 0.8333333 2.083333 5
## [5196] {train,
## dataset} => {neural} 0.1666667 0.8333333 2.500000 5
## [5197] {neural,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [5198] {train,
## neural} => {propos} 0.1666667 0.8333333 1.666667 5
## [5199] {neural,
## propos} => {train} 0.1666667 0.8333333 2.083333 5
## [5200] {train,
## propos} => {neural} 0.1666667 0.7142857 2.142857 5
## [5201] {train,
## neural} => {network} 0.1666667 0.8333333 1.315789 5
## [5202] {neural,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [5203] {show,
## neural} => {approach} 0.1333333 0.8000000 2.000000 4
## [5204] {approach,
## neural} => {propos} 0.1666667 0.8333333 1.666667 5
## [5205] {neural,
## propos} => {approach} 0.1666667 0.8333333 2.083333 5
## [5206] {approach,
## neural} => {network} 0.2000000 1.0000000 1.578947 6
## [5207] {approach,
## network} => {neural} 0.2000000 0.7500000 2.250000 6
## [5208] {neural,
## work} => {network} 0.1666667 1.0000000 1.578947 5
## [5209] {neural,
## perform} => {dataset} 0.1333333 1.0000000 2.307692 4
## [5210] {neural,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [5211] {neural,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [5212] {neural,
## perform} => {network} 0.1000000 0.7500000 1.184211 3
## [5213] {data,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5214] {data,
## neural} => {represent} 0.1000000 1.0000000 2.000000 3
## [5215] {dataset,
## neural} => {propos} 0.1666667 0.8333333 1.666667 5
## [5216] {neural,
## propos} => {dataset} 0.1666667 0.8333333 1.923077 5
## [5217] {dataset,
## neural} => {network} 0.1666667 0.8333333 1.315789 5
## [5218] {neural,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [5219] {neural,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [5220] {neural,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [5221] {neural,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [5222] {featur,
## neural} => {represent} 0.1333333 0.8000000 1.600000 4
## [5223] {represent,
## neural} => {network} 0.1666667 0.8333333 1.315789 5
## [5224] {show,
## neural} => {network} 0.1666667 1.0000000 1.578947 5
## [5225] {neural,
## propos} => {network} 0.1666667 0.8333333 1.315789 5
## [5226] {model,
## neural} => {network} 0.1333333 0.8000000 1.263158 4
## [5227] {featur,
## neural} => {network} 0.1333333 0.8000000 1.263158 4
## [5228] {method,
## algorithm} => {perform} 0.1000000 0.7500000 1.607143 3
## [5229] {method,
## algorithm} => {show} 0.1333333 1.0000000 1.875000 4
## [5230] {method,
## algorithm} => {model} 0.1000000 0.7500000 1.406250 3
## [5231] {method,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [5232] {method,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [5233] {method,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [5234] {method,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [5235] {method,
## train} => {approach} 0.1000000 0.7500000 1.875000 3
## [5236] {method,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [5237] {method,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [5238] {approach,
## method} => {dataset} 0.1666667 0.7142857 1.648352 5
## [5239] {method,
## dataset} => {approach} 0.1666667 0.8333333 2.083333 5
## [5240] {method,
## learn} => {approach} 0.1333333 0.8000000 2.000000 4
## [5241] {method,
## represent} => {approach} 0.1333333 0.8000000 2.000000 4
## [5242] {approach,
## method} => {show} 0.2000000 0.8571429 1.607143 6
## [5243] {approach,
## show} => {method} 0.2000000 0.7500000 2.045455 6
## [5244] {approach,
## method} => {propos} 0.1666667 0.7142857 1.428571 5
## [5245] {method,
## propos} => {approach} 0.1666667 0.7142857 1.785714 5
## [5246] {approach,
## method} => {model} 0.1666667 0.7142857 1.339286 5
## [5247] {method,
## model} => {approach} 0.1666667 0.7142857 1.785714 5
## [5248] {approach,
## method} => {featur} 0.1666667 0.7142857 1.339286 5
## [5249] {featur,
## method} => {approach} 0.1666667 0.7142857 1.785714 5
## [5250] {approach,
## method} => {network} 0.1666667 0.7142857 1.127820 5
## [5251] {method,
## network} => {approach} 0.1666667 0.8333333 2.083333 5
## [5252] {method,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5253] {method,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [5254] {method,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [5255] {method,
## perform} => {dataset} 0.1666667 0.7142857 1.648352 5
## [5256] {method,
## dataset} => {perform} 0.1666667 0.8333333 1.785714 5
## [5257] {method,
## perform} => {show} 0.2000000 0.8571429 1.607143 6
## [5258] {show,
## perform} => {method} 0.2000000 0.7500000 2.045455 6
## [5259] {method,
## perform} => {propos} 0.1666667 0.7142857 1.428571 5
## [5260] {method,
## propos} => {perform} 0.1666667 0.7142857 1.530612 5
## [5261] {data,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [5262] {data,
## method} => {model} 0.1000000 1.0000000 1.875000 3
## [5263] {method,
## dataset} => {show} 0.1666667 0.8333333 1.562500 5
## [5264] {show,
## dataset} => {method} 0.1666667 0.7142857 1.948052 5
## [5265] {method,
## dataset} => {propos} 0.1666667 0.8333333 1.666667 5
## [5266] {method,
## propos} => {dataset} 0.1666667 0.7142857 1.648352 5
## [5267] {method,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [5268] {method,
## represent} => {learn} 0.1333333 0.8000000 1.846154 4
## [5269] {method,
## learn} => {show} 0.1333333 0.8000000 1.500000 4
## [5270] {method,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [5271] {method,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [5272] {method,
## represent} => {show} 0.1333333 0.8000000 1.500000 4
## [5273] {method,
## represent} => {propos} 0.1333333 0.8000000 1.600000 4
## [5274] {method,
## represent} => {featur} 0.1333333 0.8000000 1.500000 4
## [5275] {method,
## propos} => {show} 0.1666667 0.7142857 1.339286 5
## [5276] {method,
## show} => {model} 0.2333333 0.7777778 1.458333 7
## [5277] {method,
## model} => {show} 0.2333333 1.0000000 1.875000 7
## [5278] {featur,
## method} => {show} 0.1666667 0.7142857 1.339286 5
## [5279] {method,
## propos} => {featur} 0.1666667 0.7142857 1.339286 5
## [5280] {featur,
## method} => {propos} 0.1666667 0.7142857 1.428571 5
## [5281] {method,
## propos} => {network} 0.1666667 0.7142857 1.127820 5
## [5282] {method,
## network} => {propos} 0.1666667 0.8333333 1.666667 5
## [5283] {featur,
## method} => {network} 0.1666667 0.7142857 1.127820 5
## [5284] {method,
## network} => {featur} 0.1666667 0.8333333 1.562500 5
## [5285] {task,
## algorithm} => {data} 0.1000000 1.0000000 2.307692 3
## [5286] {task,
## algorithm} => {featur} 0.1000000 1.0000000 1.875000 3
## [5287] {algorithm,
## work} => {train} 0.1000000 0.7500000 1.875000 3
## [5288] {algorithm,
## dataset} => {train} 0.1000000 0.7500000 1.875000 3
## [5289] {algorithm,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [5290] {approach,
## algorithm} => {propos} 0.1333333 0.8000000 1.600000 4
## [5291] {algorithm,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [5292] {approach,
## algorithm} => {network} 0.1333333 0.8000000 1.263158 4
## [5293] {algorithm,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [5294] {data,
## algorithm} => {perform} 0.1333333 0.8000000 1.714286 4
## [5295] {algorithm,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [5296] {algorithm,
## propos} => {perform} 0.1333333 0.8000000 1.714286 4
## [5297] {represent,
## algorithm} => {data} 0.1000000 0.7500000 1.730769 3
## [5298] {data,
## algorithm} => {featur} 0.1333333 0.8000000 1.500000 4
## [5299] {algorithm,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [5300] {algorithm,
## dataset} => {network} 0.1333333 1.0000000 1.578947 4
## [5301] {algorithm,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [5302] {algorithm,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [5303] {algorithm,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [5304] {represent,
## algorithm} => {featur} 0.1333333 1.0000000 1.875000 4
## [5305] {show,
## algorithm} => {model} 0.2000000 0.8571429 1.607143 6
## [5306] {model,
## algorithm} => {show} 0.2000000 0.8571429 1.607143 6
## [5307] {task,
## train} => {data} 0.1333333 0.8000000 1.846154 4
## [5308] {task,
## train} => {learn} 0.1333333 0.8000000 1.846154 4
## [5309] {train,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [5310] {task,
## train} => {represent} 0.1333333 0.8000000 1.600000 4
## [5311] {task,
## train} => {show} 0.1333333 0.8000000 1.500000 4
## [5312] {task,
## train} => {featur} 0.1333333 0.8000000 1.500000 4
## [5313] {task,
## train} => {network} 0.1333333 0.8000000 1.263158 4
## [5314] {task,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [5315] {approach,
## task} => {data} 0.1666667 0.8333333 1.923077 5
## [5316] {approach,
## data} => {task} 0.1666667 1.0000000 2.727273 5
## [5317] {approach,
## task} => {represent} 0.2000000 1.0000000 2.000000 6
## [5318] {represent,
## task} => {approach} 0.2000000 0.7500000 1.875000 6
## [5319] {approach,
## represent} => {task} 0.2000000 0.7500000 2.045455 6
## [5320] {approach,
## task} => {propos} 0.1666667 0.8333333 1.666667 5
## [5321] {task,
## propos} => {approach} 0.1666667 0.7142857 1.785714 5
## [5322] {approach,
## task} => {featur} 0.1666667 0.8333333 1.562500 5
## [5323] {task,
## work} => {data} 0.1333333 1.0000000 2.307692 4
## [5324] {data,
## work} => {task} 0.1333333 0.8000000 2.181818 4
## [5325] {task,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [5326] {task,
## dataset} => {work} 0.1333333 0.8000000 2.000000 4
## [5327] {task,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [5328] {work,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [5329] {task,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [5330] {task,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [5331] {task,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [5332] {task,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [5333] {task,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [5334] {task,
## perform} => {data} 0.1000000 1.0000000 2.307692 3
## [5335] {task,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [5336] {task,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [5337] {task,
## dataset} => {data} 0.1666667 1.0000000 2.307692 5
## [5338] {data,
## dataset} => {task} 0.1666667 0.7142857 1.948052 5
## [5339] {data,
## task} => {learn} 0.2333333 0.7000000 1.615385 7
## [5340] {task,
## learn} => {data} 0.2333333 0.8750000 2.019231 7
## [5341] {data,
## learn} => {task} 0.2333333 0.8750000 2.386364 7
## [5342] {data,
## task} => {represent} 0.2333333 0.7000000 1.400000 7
## [5343] {represent,
## task} => {data} 0.2333333 0.8750000 2.019231 7
## [5344] {data,
## represent} => {task} 0.2333333 0.7777778 2.121212 7
## [5345] {show,
## task} => {data} 0.2000000 1.0000000 2.307692 6
## [5346] {data,
## show} => {task} 0.2000000 0.8571429 2.337662 6
## [5347] {task,
## propos} => {data} 0.2000000 0.8571429 1.978022 6
## [5348] {data,
## propos} => {task} 0.2000000 0.8571429 2.337662 6
## [5349] {model,
## task} => {data} 0.2000000 1.0000000 2.307692 6
## [5350] {data,
## model} => {task} 0.2000000 0.7500000 2.045455 6
## [5351] {data,
## task} => {featur} 0.2666667 0.8000000 1.500000 8
## [5352] {featur,
## task} => {data} 0.2666667 0.8888889 2.051282 8
## [5353] {data,
## featur} => {task} 0.2666667 0.8000000 2.181818 8
## [5354] {network,
## task} => {data} 0.2000000 0.8571429 1.978022 6
## [5355] {data,
## network} => {task} 0.2000000 0.8571429 2.337662 6
## [5356] {task,
## dataset} => {learn} 0.1666667 1.0000000 2.307692 5
## [5357] {task,
## dataset} => {represent} 0.1333333 0.8000000 1.600000 4
## [5358] {task,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [5359] {task,
## dataset} => {featur} 0.1333333 0.8000000 1.500000 4
## [5360] {task,
## dataset} => {network} 0.1333333 0.8000000 1.263158 4
## [5361] {task,
## learn} => {represent} 0.2000000 0.7500000 1.500000 6
## [5362] {represent,
## task} => {learn} 0.2000000 0.7500000 1.730769 6
## [5363] {show,
## task} => {learn} 0.1666667 0.8333333 1.923077 5
## [5364] {task,
## learn} => {propos} 0.2000000 0.7500000 1.500000 6
## [5365] {task,
## propos} => {learn} 0.2000000 0.8571429 1.978022 6
## [5366] {model,
## task} => {learn} 0.1666667 0.8333333 1.923077 5
## [5367] {task,
## learn} => {featur} 0.2333333 0.8750000 1.640625 7
## [5368] {featur,
## task} => {learn} 0.2333333 0.7777778 1.794872 7
## [5369] {network,
## task} => {learn} 0.1666667 0.7142857 1.648352 5
## [5370] {network,
## learn} => {task} 0.1666667 0.8333333 2.272727 5
## [5371] {show,
## task} => {represent} 0.1666667 0.8333333 1.666667 5
## [5372] {task,
## propos} => {represent} 0.1666667 0.7142857 1.428571 5
## [5373] {represent,
## task} => {featur} 0.2333333 0.8750000 1.640625 7
## [5374] {featur,
## task} => {represent} 0.2333333 0.7777778 1.555556 7
## [5375] {network,
## task} => {represent} 0.1666667 0.7142857 1.428571 5
## [5376] {show,
## task} => {featur} 0.1666667 0.8333333 1.562500 5
## [5377] {task,
## propos} => {featur} 0.2000000 0.8571429 1.607143 6
## [5378] {model,
## task} => {featur} 0.2000000 1.0000000 1.875000 6
## [5379] {network,
## task} => {featur} 0.1666667 0.7142857 1.339286 5
## [5380] {approach,
## train} => {propos} 0.1333333 0.8000000 1.600000 4
## [5381] {approach,
## train} => {network} 0.1666667 1.0000000 1.578947 5
## [5382] {train,
## work} => {network} 0.1333333 0.8000000 1.263158 4
## [5383] {train,
## perform} => {propos} 0.1333333 0.8000000 1.600000 4
## [5384] {train,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [5385] {data,
## train} => {represent} 0.1666667 0.8333333 1.666667 5
## [5386] {represent,
## train} => {data} 0.1666667 0.7142857 1.648352 5
## [5387] {data,
## train} => {featur} 0.1666667 0.8333333 1.562500 5
## [5388] {featur,
## train} => {data} 0.1666667 0.8333333 1.923077 5
## [5389] {train,
## dataset} => {network} 0.1666667 0.8333333 1.315789 5
## [5390] {train,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [5391] {train,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [5392] {train,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [5393] {represent,
## train} => {featur} 0.1666667 0.7142857 1.339286 5
## [5394] {featur,
## train} => {represent} 0.1666667 0.8333333 1.666667 5
## [5395] {represent,
## train} => {network} 0.1666667 0.7142857 1.127820 5
## [5396] {approach,
## work} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5397] {approach,
## work} => {learn} 0.1333333 0.8000000 1.846154 4
## [5398] {work,
## learn} => {approach} 0.1333333 0.8000000 2.000000 4
## [5399] {approach,
## work} => {represent} 0.1333333 0.8000000 1.600000 4
## [5400] {approach,
## work} => {show} 0.1333333 0.8000000 1.500000 4
## [5401] {show,
## work} => {approach} 0.1333333 0.8000000 2.000000 4
## [5402] {approach,
## work} => {propos} 0.1333333 0.8000000 1.600000 4
## [5403] {approach,
## work} => {network} 0.1333333 0.8000000 1.263158 4
## [5404] {approach,
## perform} => {dataset} 0.1666667 0.8333333 1.923077 5
## [5405] {approach,
## perform} => {propos} 0.1666667 0.8333333 1.666667 5
## [5406] {approach,
## data} => {represent} 0.1666667 1.0000000 2.000000 5
## [5407] {approach,
## data} => {propos} 0.1333333 0.8000000 1.600000 4
## [5408] {approach,
## data} => {featur} 0.1333333 0.8000000 1.500000 4
## [5409] {approach,
## learn} => {dataset} 0.1666667 0.7142857 1.648352 5
## [5410] {approach,
## dataset} => {show} 0.2000000 0.7500000 1.406250 6
## [5411] {approach,
## show} => {dataset} 0.2000000 0.7500000 1.730769 6
## [5412] {show,
## dataset} => {approach} 0.2000000 0.8571429 2.142857 6
## [5413] {approach,
## dataset} => {propos} 0.2333333 0.8750000 1.750000 7
## [5414] {approach,
## propos} => {dataset} 0.2333333 0.7777778 1.794872 7
## [5415] {dataset,
## propos} => {approach} 0.2333333 0.7000000 1.750000 7
## [5416] {approach,
## dataset} => {model} 0.2000000 0.7500000 1.406250 6
## [5417] {approach,
## model} => {dataset} 0.2000000 0.7500000 1.730769 6
## [5418] {model,
## dataset} => {approach} 0.2000000 0.7500000 1.875000 6
## [5419] {approach,
## learn} => {represent} 0.2000000 0.8571429 1.714286 6
## [5420] {approach,
## represent} => {learn} 0.2000000 0.7500000 1.730769 6
## [5421] {approach,
## learn} => {show} 0.1666667 0.7142857 1.339286 5
## [5422] {approach,
## learn} => {propos} 0.1666667 0.7142857 1.428571 5
## [5423] {approach,
## learn} => {model} 0.1666667 0.7142857 1.339286 5
## [5424] {approach,
## learn} => {featur} 0.1666667 0.7142857 1.339286 5
## [5425] {approach,
## represent} => {propos} 0.2000000 0.7500000 1.500000 6
## [5426] {approach,
## represent} => {featur} 0.2000000 0.7500000 1.406250 6
## [5427] {approach,
## featur} => {represent} 0.2000000 0.7500000 1.500000 6
## [5428] {approach,
## show} => {model} 0.2000000 0.7500000 1.406250 6
## [5429] {approach,
## model} => {show} 0.2000000 0.7500000 1.406250 6
## [5430] {approach,
## show} => {network} 0.2000000 0.7500000 1.184211 6
## [5431] {approach,
## network} => {show} 0.2000000 0.7500000 1.406250 6
## [5432] {network,
## show} => {approach} 0.2000000 0.7500000 1.875000 6
## [5433] {approach,
## network} => {propos} 0.2000000 0.7500000 1.500000 6
## [5434] {network,
## propos} => {approach} 0.2000000 0.7500000 1.875000 6
## [5435] {approach,
## model} => {featur} 0.2000000 0.7500000 1.406250 6
## [5436] {approach,
## featur} => {model} 0.2000000 0.7500000 1.406250 6
## [5437] {network,
## perform} => {work} 0.1333333 0.8000000 2.000000 4
## [5438] {data,
## work} => {dataset} 0.1666667 1.0000000 2.307692 5
## [5439] {dataset,
## work} => {data} 0.1666667 0.7142857 1.648352 5
## [5440] {data,
## dataset} => {work} 0.1666667 0.7142857 1.785714 5
## [5441] {data,
## work} => {learn} 0.1333333 0.8000000 1.846154 4
## [5442] {work,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [5443] {data,
## work} => {represent} 0.1333333 0.8000000 1.600000 4
## [5444] {data,
## work} => {propos} 0.1333333 0.8000000 1.600000 4
## [5445] {data,
## work} => {featur} 0.1333333 0.8000000 1.500000 4
## [5446] {data,
## work} => {network} 0.1333333 0.8000000 1.263158 4
## [5447] {work,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5448] {dataset,
## work} => {propos} 0.2000000 0.8571429 1.714286 6
## [5449] {propos,
## work} => {dataset} 0.2000000 0.8571429 1.978022 6
## [5450] {dataset,
## work} => {featur} 0.1666667 0.7142857 1.339286 5
## [5451] {featur,
## work} => {dataset} 0.1666667 0.8333333 1.923077 5
## [5452] {dataset,
## work} => {network} 0.2000000 0.8571429 1.353383 6
## [5453] {work,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [5454] {work,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [5455] {work,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [5456] {model,
## work} => {learn} 0.1333333 0.8000000 1.846154 4
## [5457] {work,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [5458] {work,
## learn} => {network} 0.1333333 0.8000000 1.263158 4
## [5459] {show,
## work} => {represent} 0.1333333 0.8000000 1.600000 4
## [5460] {show,
## work} => {propos} 0.1333333 0.8000000 1.600000 4
## [5461] {show,
## work} => {network} 0.1333333 0.8000000 1.263158 4
## [5462] {propos,
## work} => {network} 0.1666667 0.7142857 1.127820 5
## [5463] {model,
## work} => {featur} 0.1333333 0.8000000 1.500000 4
## [5464] {model,
## work} => {network} 0.1333333 0.8000000 1.263158 4
## [5465] {featur,
## work} => {network} 0.1666667 0.8333333 1.315789 5
## [5466] {data,
## perform} => {featur} 0.1666667 0.8333333 1.562500 5
## [5467] {dataset,
## perform} => {propos} 0.2000000 0.7500000 1.500000 6
## [5468] {dataset,
## perform} => {featur} 0.2000000 0.7500000 1.406250 6
## [5469] {network,
## perform} => {dataset} 0.1333333 0.8000000 1.846154 4
## [5470] {perform,
## learn} => {propos} 0.1666667 0.8333333 1.666667 5
## [5471] {perform,
## learn} => {model} 0.1666667 0.8333333 1.562500 5
## [5472] {perform,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [5473] {represent,
## perform} => {propos} 0.2000000 0.8571429 1.714286 6
## [5474] {represent,
## perform} => {featur} 0.1666667 0.7142857 1.339286 5
## [5475] {show,
## perform} => {propos} 0.2000000 0.7500000 1.500000 6
## [5476] {show,
## propos} => {perform} 0.2000000 0.7500000 1.607143 6
## [5477] {perform,
## propos} => {featur} 0.2333333 0.7000000 1.312500 7
## [5478] {featur,
## perform} => {propos} 0.2333333 0.7777778 1.555556 7
## [5479] {featur,
## propos} => {perform} 0.2333333 0.7000000 1.500000 7
## [5480] {data,
## dataset} => {learn} 0.2000000 0.8571429 1.978022 6
## [5481] {data,
## learn} => {dataset} 0.2000000 0.7500000 1.730769 6
## [5482] {dataset,
## learn} => {data} 0.2000000 0.7500000 1.730769 6
## [5483] {data,
## dataset} => {represent} 0.2000000 0.8571429 1.714286 6
## [5484] {represent,
## dataset} => {data} 0.2000000 0.8571429 1.978022 6
## [5485] {data,
## dataset} => {propos} 0.1666667 0.7142857 1.428571 5
## [5486] {data,
## propos} => {dataset} 0.1666667 0.7142857 1.648352 5
## [5487] {data,
## dataset} => {featur} 0.2000000 0.8571429 1.607143 6
## [5488] {data,
## dataset} => {network} 0.1666667 0.7142857 1.127820 5
## [5489] {data,
## network} => {dataset} 0.1666667 0.7142857 1.648352 5
## [5490] {data,
## learn} => {represent} 0.2000000 0.7500000 1.500000 6
## [5491] {data,
## show} => {learn} 0.1666667 0.7142857 1.648352 5
## [5492] {data,
## learn} => {propos} 0.2000000 0.7500000 1.500000 6
## [5493] {data,
## propos} => {learn} 0.2000000 0.8571429 1.978022 6
## [5494] {data,
## learn} => {model} 0.2000000 0.7500000 1.406250 6
## [5495] {data,
## model} => {learn} 0.2000000 0.7500000 1.730769 6
## [5496] {data,
## learn} => {featur} 0.2333333 0.8750000 1.640625 7
## [5497] {data,
## featur} => {learn} 0.2333333 0.7000000 1.615385 7
## [5498] {data,
## show} => {represent} 0.1666667 0.7142857 1.428571 5
## [5499] {data,
## propos} => {represent} 0.1666667 0.7142857 1.428571 5
## [5500] {data,
## represent} => {featur} 0.2666667 0.8888889 1.666667 8
## [5501] {data,
## featur} => {represent} 0.2666667 0.8000000 1.600000 8
## [5502] {featur,
## represent} => {data} 0.2666667 0.7272727 1.678322 8
## [5503] {data,
## network} => {represent} 0.1666667 0.7142857 1.428571 5
## [5504] {data,
## show} => {model} 0.1666667 0.7142857 1.339286 5
## [5505] {data,
## show} => {featur} 0.1666667 0.7142857 1.339286 5
## [5506] {data,
## propos} => {model} 0.1666667 0.7142857 1.339286 5
## [5507] {data,
## propos} => {featur} 0.2000000 0.8571429 1.607143 6
## [5508] {data,
## model} => {featur} 0.2333333 0.8750000 1.640625 7
## [5509] {data,
## featur} => {model} 0.2333333 0.7000000 1.312500 7
## [5510] {featur,
## model} => {data} 0.2333333 0.7000000 1.615385 7
## [5511] {data,
## network} => {featur} 0.1666667 0.7142857 1.339286 5
## [5512] {dataset,
## learn} => {represent} 0.2000000 0.7500000 1.500000 6
## [5513] {represent,
## dataset} => {learn} 0.2000000 0.8571429 1.978022 6
## [5514] {show,
## dataset} => {learn} 0.1666667 0.7142857 1.648352 5
## [5515] {dataset,
## learn} => {propos} 0.2000000 0.7500000 1.500000 6
## [5516] {dataset,
## learn} => {model} 0.2000000 0.7500000 1.406250 6
## [5517] {model,
## dataset} => {learn} 0.2000000 0.7500000 1.730769 6
## [5518] {dataset,
## learn} => {featur} 0.2000000 0.7500000 1.406250 6
## [5519] {represent,
## dataset} => {propos} 0.1666667 0.7142857 1.428571 5
## [5520] {represent,
## dataset} => {featur} 0.1666667 0.7142857 1.339286 5
## [5521] {show,
## dataset} => {propos} 0.1666667 0.7142857 1.428571 5
## [5522] {show,
## dataset} => {network} 0.1666667 0.7142857 1.127820 5
## [5523] {dataset,
## propos} => {model} 0.2333333 0.7000000 1.312500 7
## [5524] {model,
## dataset} => {propos} 0.2333333 0.8750000 1.750000 7
## [5525] {model,
## propos} => {dataset} 0.2333333 0.8750000 2.019231 7
## [5526] {dataset,
## propos} => {network} 0.2333333 0.7000000 1.105263 7
## [5527] {network,
## dataset} => {propos} 0.2333333 0.7777778 1.555556 7
## [5528] {network,
## propos} => {dataset} 0.2333333 0.8750000 2.019231 7
## [5529] {model,
## dataset} => {featur} 0.2000000 0.7500000 1.406250 6
## [5530] {represent,
## learn} => {show} 0.2333333 0.7000000 1.312500 7
## [5531] {show,
## learn} => {represent} 0.2333333 0.7777778 1.555556 7
## [5532] {represent,
## show} => {learn} 0.2333333 0.7000000 1.615385 7
## [5533] {represent,
## learn} => {propos} 0.2333333 0.7000000 1.400000 7
## [5534] {propos,
## learn} => {represent} 0.2333333 0.7777778 1.555556 7
## [5535] {represent,
## propos} => {learn} 0.2333333 0.7777778 1.794872 7
## [5536] {model,
## represent} => {learn} 0.2000000 0.7500000 1.730769 6
## [5537] {represent,
## learn} => {featur} 0.2666667 0.8000000 1.500000 8
## [5538] {featur,
## learn} => {represent} 0.2666667 0.7272727 1.454545 8
## [5539] {featur,
## represent} => {learn} 0.2666667 0.7272727 1.678322 8
## [5540] {network,
## learn} => {represent} 0.1666667 0.8333333 1.666667 5
## [5541] {show,
## learn} => {featur} 0.2333333 0.7777778 1.458333 7
## [5542] {featur,
## show} => {learn} 0.2333333 0.7777778 1.794872 7
## [5543] {model,
## propos} => {learn} 0.2000000 0.7500000 1.730769 6
## [5544] {propos,
## learn} => {featur} 0.2333333 0.7777778 1.458333 7
## [5545] {featur,
## propos} => {learn} 0.2333333 0.7000000 1.615385 7
## [5546] {model,
## learn} => {featur} 0.2666667 0.8888889 1.666667 8
## [5547] {featur,
## learn} => {model} 0.2666667 0.7272727 1.363636 8
## [5548] {featur,
## model} => {learn} 0.2666667 0.8000000 1.846154 8
## [5549] {network,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [5550] {model,
## represent} => {show} 0.2000000 0.7500000 1.406250 6
## [5551] {network,
## represent} => {show} 0.2000000 0.7500000 1.406250 6
## [5552] {network,
## show} => {represent} 0.2000000 0.7500000 1.500000 6
## [5553] {model,
## represent} => {featur} 0.2000000 0.7500000 1.406250 6
## [5554] {network,
## represent} => {featur} 0.2000000 0.7500000 1.406250 6
## [5555] {featur,
## show} => {model} 0.2333333 0.7777778 1.458333 7
## [5556] {featur,
## model} => {show} 0.2333333 0.7000000 1.312500 7
## [5557] {model,
## propos} => {featur} 0.2000000 0.7500000 1.406250 6
## [5558] {method,
## problem,
## shown} => {show} 0.1000000 1.0000000 1.875000 3
## [5559] {show,
## problem,
## shown} => {method} 0.1000000 1.0000000 2.727273 3
## [5560] {method,
## show,
## shown} => {problem} 0.1000000 1.0000000 3.333333 3
## [5561] {method,
## problem,
## convent} => {show} 0.1000000 1.0000000 1.875000 3
## [5562] {show,
## problem,
## convent} => {method} 0.1000000 1.0000000 2.727273 3
## [5563] {method,
## show,
## convent} => {problem} 0.1000000 1.0000000 3.333333 3
## [5564] {dataset,
## perform,
## current} => {featur} 0.1000000 1.0000000 1.875000 3
## [5565] {featur,
## perform,
## current} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5566] {featur,
## dataset,
## current} => {perform} 0.1000000 1.0000000 2.142857 3
## [5567] {infer,
## make,
## paper} => {data} 0.1000000 1.0000000 2.307692 3
## [5568] {data,
## infer,
## make} => {paper} 0.1000000 1.0000000 3.000000 3
## [5569] {data,
## infer,
## paper} => {make} 0.1000000 1.0000000 3.333333 3
## [5570] {data,
## make,
## paper} => {infer} 0.1000000 1.0000000 10.000000 3
## [5571] {infer,
## make,
## paper} => {model} 0.1000000 1.0000000 1.875000 3
## [5572] {infer,
## make,
## model} => {paper} 0.1000000 1.0000000 3.000000 3
## [5573] {infer,
## model,
## paper} => {make} 0.1000000 1.0000000 3.333333 3
## [5574] {make,
## model,
## paper} => {infer} 0.1000000 0.7500000 7.500000 3
## [5575] {data,
## infer,
## make} => {model} 0.1000000 1.0000000 1.875000 3
## [5576] {infer,
## make,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [5577] {data,
## infer,
## model} => {make} 0.1000000 1.0000000 3.333333 3
## [5578] {data,
## make,
## model} => {infer} 0.1000000 0.7500000 7.500000 3
## [5579] {data,
## infer,
## paper} => {model} 0.1000000 1.0000000 1.875000 3
## [5580] {infer,
## model,
## paper} => {data} 0.1000000 1.0000000 2.307692 3
## [5581] {data,
## infer,
## model} => {paper} 0.1000000 1.0000000 3.000000 3
## [5582] {data,
## model,
## paper} => {infer} 0.1000000 0.7500000 7.500000 3
## [5583] {approach,
## larger,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5584] {dataset,
## larger,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [5585] {approach,
## dataset,
## larger} => {result} 0.1000000 1.0000000 3.000000 3
## [5586] {approach,
## dataset,
## result} => {larger} 0.1000000 0.7500000 7.500000 3
## [5587] {approach,
## larger,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [5588] {larger,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [5589] {approach,
## larger,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [5590] {approach,
## larger,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [5591] {model,
## larger,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [5592] {approach,
## model,
## larger} => {result} 0.1000000 1.0000000 3.000000 3
## [5593] {approach,
## model,
## result} => {larger} 0.1000000 1.0000000 10.000000 3
## [5594] {dataset,
## larger,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [5595] {larger,
## propos,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5596] {dataset,
## larger,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [5597] {dataset,
## propos,
## result} => {larger} 0.1000000 0.7500000 7.500000 3
## [5598] {dataset,
## larger,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [5599] {model,
## larger,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5600] {model,
## dataset,
## larger} => {result} 0.1000000 1.0000000 3.000000 3
## [5601] {model,
## dataset,
## result} => {larger} 0.1000000 1.0000000 10.000000 3
## [5602] {larger,
## propos,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [5603] {model,
## larger,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [5604] {model,
## larger,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [5605] {model,
## propos,
## result} => {larger} 0.1000000 1.0000000 10.000000 3
## [5606] {approach,
## dataset,
## larger} => {propos} 0.1000000 1.0000000 2.000000 3
## [5607] {approach,
## larger,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5608] {dataset,
## larger,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [5609] {approach,
## dataset,
## larger} => {model} 0.1000000 1.0000000 1.875000 3
## [5610] {approach,
## model,
## larger} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5611] {model,
## dataset,
## larger} => {approach} 0.1000000 1.0000000 2.500000 3
## [5612] {approach,
## larger,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [5613] {approach,
## model,
## larger} => {propos} 0.1000000 1.0000000 2.000000 3
## [5614] {model,
## larger,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [5615] {dataset,
## larger,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [5616] {model,
## dataset,
## larger} => {propos} 0.1000000 1.0000000 2.000000 3
## [5617] {model,
## larger,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5618] {data,
## label,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [5619] {featur,
## label,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [5620] {data,
## featur,
## label} => {task} 0.1000000 1.0000000 2.727273 3
## [5621] {challeng,
## face,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [5622] {recognit,
## face,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [5623] {recognit,
## challeng,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [5624] {recognit,
## challeng,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [5625] {challeng,
## face,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [5626] {train,
## face,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [5627] {train,
## challeng,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [5628] {train,
## challeng,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [5629] {challeng,
## face,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [5630] {represent,
## face,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [5631] {represent,
## challeng,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [5632] {represent,
## challeng,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [5633] {recognit,
## face,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [5634] {train,
## face,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [5635] {train,
## recognit,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [5636] {train,
## recognit,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [5637] {recognit,
## face,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [5638] {represent,
## face,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [5639] {represent,
## recognit,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [5640] {represent,
## recognit,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [5641] {train,
## face,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [5642] {represent,
## face,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [5643] {represent,
## train,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [5644] {represent,
## train,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [5645] {recognit,
## challeng,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [5646] {train,
## challeng,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [5647] {train,
## recognit,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [5648] {train,
## recognit,
## challeng} => {ident} 0.1000000 1.0000000 10.000000 3
## [5649] {recognit,
## challeng,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [5650] {represent,
## challeng,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [5651] {represent,
## recognit,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [5652] {represent,
## recognit,
## challeng} => {ident} 0.1000000 1.0000000 10.000000 3
## [5653] {train,
## challeng,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [5654] {represent,
## challeng,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [5655] {represent,
## train,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [5656] {represent,
## train,
## challeng} => {ident} 0.1000000 1.0000000 10.000000 3
## [5657] {train,
## recognit,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [5658] {represent,
## recognit,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [5659] {represent,
## train,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [5660] {represent,
## train,
## recognit} => {ident} 0.1000000 0.7500000 7.500000 3
## [5661] {make,
## problem,
## standard} => {perform} 0.1000000 1.0000000 2.142857 3
## [5662] {make,
## perform,
## standard} => {problem} 0.1000000 1.0000000 3.333333 3
## [5663] {perform,
## problem,
## standard} => {make} 0.1000000 1.0000000 3.333333 3
## [5664] {make,
## problem,
## standard} => {model} 0.1000000 1.0000000 1.875000 3
## [5665] {make,
## model,
## standard} => {problem} 0.1000000 1.0000000 3.333333 3
## [5666] {model,
## problem,
## standard} => {make} 0.1000000 1.0000000 3.333333 3
## [5667] {make,
## model,
## problem} => {standard} 0.1000000 0.7500000 7.500000 3
## [5668] {make,
## perform,
## standard} => {model} 0.1000000 1.0000000 1.875000 3
## [5669] {make,
## model,
## standard} => {perform} 0.1000000 1.0000000 2.142857 3
## [5670] {model,
## perform,
## standard} => {make} 0.1000000 1.0000000 3.333333 3
## [5671] {perform,
## problem,
## standard} => {model} 0.1000000 1.0000000 1.875000 3
## [5672] {model,
## problem,
## standard} => {perform} 0.1000000 1.0000000 2.142857 3
## [5673] {model,
## perform,
## standard} => {problem} 0.1000000 1.0000000 3.333333 3
## [5674] {dataset,
## propos,
## pose} => {featur} 0.1000000 1.0000000 1.875000 3
## [5675] {featur,
## dataset,
## pose} => {propos} 0.1000000 1.0000000 2.000000 3
## [5676] {featur,
## propos,
## pose} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5677] {improv,
## shallow,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [5678] {perform,
## shallow,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [5679] {improv,
## perform,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [5680] {improv,
## perform,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [5681] {improv,
## shallow,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5682] {dataset,
## shallow,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [5683] {dataset,
## improv,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [5684] {dataset,
## improv,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [5685] {improv,
## shallow,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [5686] {featur,
## shallow,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [5687] {featur,
## improv,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [5688] {featur,
## improv,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [5689] {perform,
## shallow,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5690] {dataset,
## shallow,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [5691] {dataset,
## perform,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [5692] {dataset,
## perform,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [5693] {perform,
## shallow,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [5694] {featur,
## shallow,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [5695] {featur,
## perform,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [5696] {featur,
## perform,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [5697] {dataset,
## shallow,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [5698] {featur,
## shallow,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5699] {featur,
## dataset,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [5700] {featur,
## dataset,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [5701] {improv,
## perform,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5702] {dataset,
## improv,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [5703] {dataset,
## perform,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [5704] {improv,
## perform,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [5705] {featur,
## improv,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [5706] {featur,
## perform,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [5707] {featur,
## improv,
## perform} => {tradit} 0.1000000 0.7500000 7.500000 3
## [5708] {dataset,
## improv,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [5709] {featur,
## improv,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5710] {featur,
## dataset,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [5711] {featur,
## dataset,
## improv} => {tradit} 0.1000000 0.7500000 7.500000 3
## [5712] {dataset,
## perform,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [5713] {featur,
## perform,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5714] {featur,
## dataset,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [5715] {represent,
## perform,
## spars} => {propos} 0.1000000 1.0000000 2.000000 3
## [5716] {perform,
## propos,
## spars} => {represent} 0.1000000 1.0000000 2.000000 3
## [5717] {represent,
## propos,
## spars} => {perform} 0.1000000 1.0000000 2.142857 3
## [5718] {perform,
## learn,
## discrimin} => {model} 0.1000000 1.0000000 1.875000 3
## [5719] {model,
## perform,
## discrimin} => {learn} 0.1000000 1.0000000 2.307692 3
## [5720] {model,
## learn,
## discrimin} => {perform} 0.1000000 1.0000000 2.142857 3
## [5721] {perform,
## learn,
## discrimin} => {featur} 0.1000000 1.0000000 1.875000 3
## [5722] {featur,
## perform,
## discrimin} => {learn} 0.1000000 1.0000000 2.307692 3
## [5723] {featur,
## learn,
## discrimin} => {perform} 0.1000000 1.0000000 2.142857 3
## [5724] {model,
## perform,
## discrimin} => {featur} 0.1000000 1.0000000 1.875000 3
## [5725] {featur,
## perform,
## discrimin} => {model} 0.1000000 1.0000000 1.875000 3
## [5726] {featur,
## model,
## discrimin} => {perform} 0.1000000 1.0000000 2.142857 3
## [5727] {model,
## learn,
## discrimin} => {featur} 0.1000000 1.0000000 1.875000 3
## [5728] {featur,
## learn,
## discrimin} => {model} 0.1000000 1.0000000 1.875000 3
## [5729] {featur,
## model,
## discrimin} => {learn} 0.1000000 1.0000000 2.307692 3
## [5730] {advantag,
## classif,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [5731] {advantag,
## approach,
## classif} => {method} 0.1000000 1.0000000 2.727273 3
## [5732] {advantag,
## approach,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [5733] {advantag,
## classif,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [5734] {advantag,
## classif,
## featur} => {method} 0.1000000 1.0000000 2.727273 3
## [5735] {advantag,
## featur,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [5736] {advantag,
## classif,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [5737] {advantag,
## classif,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [5738] {advantag,
## method,
## network} => {classif} 0.1000000 1.0000000 3.750000 3
## [5739] {classif,
## method,
## network} => {advantag} 0.1000000 0.7500000 7.500000 3
## [5740] {advantag,
## approach,
## classif} => {featur} 0.1000000 1.0000000 1.875000 3
## [5741] {advantag,
## classif,
## featur} => {approach} 0.1000000 1.0000000 2.500000 3
## [5742] {advantag,
## approach,
## featur} => {classif} 0.1000000 1.0000000 3.750000 3
## [5743] {approach,
## classif,
## featur} => {advantag} 0.1000000 0.7500000 7.500000 3
## [5744] {advantag,
## approach,
## classif} => {network} 0.1000000 1.0000000 1.578947 3
## [5745] {advantag,
## classif,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [5746] {advantag,
## approach,
## network} => {classif} 0.1000000 1.0000000 3.750000 3
## [5747] {approach,
## classif,
## network} => {advantag} 0.1000000 0.7500000 7.500000 3
## [5748] {advantag,
## classif,
## featur} => {network} 0.1000000 1.0000000 1.578947 3
## [5749] {advantag,
## classif,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [5750] {advantag,
## featur,
## network} => {classif} 0.1000000 1.0000000 3.750000 3
## [5751] {classif,
## featur,
## network} => {advantag} 0.1000000 0.7500000 7.500000 3
## [5752] {advantag,
## approach,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [5753] {advantag,
## featur,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [5754] {advantag,
## approach,
## featur} => {method} 0.1000000 1.0000000 2.727273 3
## [5755] {advantag,
## approach,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [5756] {advantag,
## method,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [5757] {advantag,
## approach,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [5758] {advantag,
## featur,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [5759] {advantag,
## method,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [5760] {advantag,
## featur,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [5761] {advantag,
## approach,
## featur} => {network} 0.1000000 1.0000000 1.578947 3
## [5762] {advantag,
## approach,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [5763] {advantag,
## featur,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [5764] {work,
## initi,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [5765] {network,
## initi,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [5766] {network,
## work,
## initi} => {layer} 0.1000000 1.0000000 5.000000 3
## [5767] {network,
## work,
## layer} => {initi} 0.1000000 0.7500000 7.500000 3
## [5768] {perform,
## nonlinear,
## power} => {featur} 0.1000000 1.0000000 1.875000 3
## [5769] {featur,
## nonlinear,
## power} => {perform} 0.1000000 1.0000000 2.142857 3
## [5770] {featur,
## perform,
## nonlinear} => {power} 0.1000000 1.0000000 7.500000 3
## [5771] {featur,
## perform,
## power} => {nonlinear} 0.1000000 1.0000000 10.000000 3
## [5772] {train,
## framework,
## finetun} => {work} 0.1000000 1.0000000 2.500000 3
## [5773] {work,
## framework,
## finetun} => {train} 0.1000000 1.0000000 2.500000 3
## [5774] {train,
## work,
## finetun} => {framework} 0.1000000 1.0000000 5.000000 3
## [5775] {train,
## work,
## framework} => {finetun} 0.1000000 1.0000000 10.000000 3
## [5776] {perform,
## imag,
## local} => {show} 0.1000000 1.0000000 1.875000 3
## [5777] {show,
## imag,
## local} => {perform} 0.1000000 1.0000000 2.142857 3
## [5778] {show,
## perform,
## local} => {imag} 0.1000000 1.0000000 6.000000 3
## [5779] {show,
## perform,
## imag} => {local} 0.1000000 1.0000000 10.000000 3
## [5780] {perform,
## imag,
## local} => {propos} 0.1000000 1.0000000 2.000000 3
## [5781] {propos,
## imag,
## local} => {perform} 0.1000000 1.0000000 2.142857 3
## [5782] {perform,
## propos,
## local} => {imag} 0.1000000 1.0000000 6.000000 3
## [5783] {perform,
## propos,
## imag} => {local} 0.1000000 0.7500000 7.500000 3
## [5784] {show,
## imag,
## local} => {propos} 0.1000000 1.0000000 2.000000 3
## [5785] {propos,
## imag,
## local} => {show} 0.1000000 1.0000000 1.875000 3
## [5786] {show,
## propos,
## local} => {imag} 0.1000000 1.0000000 6.000000 3
## [5787] {show,
## propos,
## imag} => {local} 0.1000000 1.0000000 10.000000 3
## [5788] {show,
## perform,
## local} => {propos} 0.1000000 1.0000000 2.000000 3
## [5789] {perform,
## propos,
## local} => {show} 0.1000000 1.0000000 1.875000 3
## [5790] {show,
## propos,
## local} => {perform} 0.1000000 1.0000000 2.142857 3
## [5791] {classif,
## machin,
## field} => {learn} 0.1000000 1.0000000 2.307692 3
## [5792] {machin,
## learn,
## field} => {classif} 0.1000000 1.0000000 3.750000 3
## [5793] {classif,
## learn,
## field} => {machin} 0.1000000 1.0000000 4.285714 3
## [5794] {classif,
## machin,
## learn} => {field} 0.1000000 1.0000000 10.000000 3
## [5795] {classif,
## machin,
## field} => {featur} 0.1000000 1.0000000 1.875000 3
## [5796] {featur,
## machin,
## field} => {classif} 0.1000000 1.0000000 3.750000 3
## [5797] {classif,
## featur,
## field} => {machin} 0.1000000 1.0000000 4.285714 3
## [5798] {classif,
## featur,
## machin} => {field} 0.1000000 0.7500000 7.500000 3
## [5799] {machin,
## learn,
## field} => {featur} 0.1000000 1.0000000 1.875000 3
## [5800] {featur,
## machin,
## field} => {learn} 0.1000000 1.0000000 2.307692 3
## [5801] {featur,
## learn,
## field} => {machin} 0.1000000 1.0000000 4.285714 3
## [5802] {featur,
## machin,
## learn} => {field} 0.1000000 0.7500000 7.500000 3
## [5803] {classif,
## learn,
## field} => {featur} 0.1000000 1.0000000 1.875000 3
## [5804] {classif,
## featur,
## field} => {learn} 0.1000000 1.0000000 2.307692 3
## [5805] {featur,
## learn,
## field} => {classif} 0.1000000 1.0000000 3.750000 3
## [5806] {represent,
## result,
## engin} => {featur} 0.1000000 1.0000000 1.875000 3
## [5807] {featur,
## result,
## engin} => {represent} 0.1000000 1.0000000 2.000000 3
## [5808] {featur,
## represent,
## engin} => {result} 0.1000000 1.0000000 3.000000 3
## [5809] {task,
## demonstr,
## easili} => {data} 0.1000000 1.0000000 2.307692 3
## [5810] {data,
## demonstr,
## easili} => {task} 0.1000000 1.0000000 2.727273 3
## [5811] {data,
## task,
## easili} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [5812] {data,
## task,
## demonstr} => {easili} 0.1000000 1.0000000 10.000000 3
## [5813] {data,
## present,
## under} => {show} 0.1000000 1.0000000 1.875000 3
## [5814] {present,
## show,
## under} => {data} 0.1000000 1.0000000 2.307692 3
## [5815] {data,
## show,
## under} => {present} 0.1000000 1.0000000 6.000000 3
## [5816] {data,
## present,
## show} => {under} 0.1000000 1.0000000 7.500000 3
## [5817] {method,
## show,
## under} => {model} 0.1000000 1.0000000 1.875000 3
## [5818] {method,
## model,
## under} => {show} 0.1000000 1.0000000 1.875000 3
## [5819] {model,
## show,
## under} => {method} 0.1000000 1.0000000 2.727273 3
## [5820] {data,
## task,
## transfer} => {network} 0.1000000 1.0000000 1.578947 3
## [5821] {network,
## task,
## transfer} => {data} 0.1000000 1.0000000 2.307692 3
## [5822] {data,
## network,
## transfer} => {task} 0.1000000 1.0000000 2.727273 3
## [5823] {train,
## algorithm,
## condit} => {propos} 0.1000000 1.0000000 2.000000 3
## [5824] {algorithm,
## propos,
## condit} => {train} 0.1000000 1.0000000 2.500000 3
## [5825] {train,
## propos,
## condit} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [5826] {train,
## algorithm,
## propos} => {condit} 0.1000000 1.0000000 10.000000 3
## [5827] {approach,
## dataset,
## common} => {show} 0.1000000 1.0000000 1.875000 3
## [5828] {approach,
## show,
## common} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5829] {show,
## dataset,
## common} => {approach} 0.1000000 1.0000000 2.500000 3
## [5830] {approach,
## dataset,
## common} => {propos} 0.1000000 1.0000000 2.000000 3
## [5831] {approach,
## propos,
## common} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5832] {dataset,
## propos,
## common} => {approach} 0.1000000 1.0000000 2.500000 3
## [5833] {approach,
## show,
## common} => {propos} 0.1000000 1.0000000 2.000000 3
## [5834] {approach,
## propos,
## common} => {show} 0.1000000 1.0000000 1.875000 3
## [5835] {show,
## propos,
## common} => {approach} 0.1000000 1.0000000 2.500000 3
## [5836] {show,
## dataset,
## common} => {propos} 0.1000000 1.0000000 2.000000 3
## [5837] {dataset,
## propos,
## common} => {show} 0.1000000 1.0000000 1.875000 3
## [5838] {show,
## propos,
## common} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5839] {data,
## dataset,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [5840] {data,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5841] {dataset,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [5842] {data,
## dataset,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [5843] {data,
## propos,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5844] {dataset,
## propos,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [5845] {data,
## dataset,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [5846] {data,
## model,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5847] {model,
## dataset,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [5848] {data,
## model,
## dataset} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [5849] {data,
## dataset,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [5850] {data,
## featur,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5851] {featur,
## dataset,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [5852] {data,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [5853] {data,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [5854] {propos,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [5855] {data,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [5856] {data,
## model,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [5857] {model,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [5858] {data,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [5859] {data,
## featur,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [5860] {featur,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [5861] {data,
## propos,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [5862] {data,
## model,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [5863] {model,
## propos,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [5864] {data,
## propos,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [5865] {data,
## featur,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [5866] {featur,
## propos,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [5867] {data,
## model,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [5868] {data,
## featur,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [5869] {featur,
## model,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [5870] {dataset,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [5871] {dataset,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [5872] {propos,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5873] {dataset,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [5874] {model,
## dataset,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [5875] {model,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5876] {dataset,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [5877] {featur,
## dataset,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [5878] {featur,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5879] {dataset,
## propos,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [5880] {model,
## dataset,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [5881] {model,
## propos,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5882] {dataset,
## propos,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [5883] {featur,
## dataset,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [5884] {featur,
## propos,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5885] {model,
## dataset,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [5886] {featur,
## dataset,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [5887] {featur,
## model,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5888] {propos,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [5889] {model,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [5890] {model,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [5891] {propos,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [5892] {featur,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [5893] {featur,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [5894] {model,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [5895] {featur,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [5896] {featur,
## model,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [5897] {model,
## propos,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [5898] {featur,
## propos,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [5899] {featur,
## model,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [5900] {compon,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [5901] {compon,
## experi,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5902] {compon,
## dataset,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [5903] {dataset,
## experi,
## propos} => {compon} 0.1000000 0.7500000 7.500000 3
## [5904] {compon,
## dataset,
## experi} => {model} 0.1000000 1.0000000 1.875000 3
## [5905] {model,
## compon,
## experi} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5906] {model,
## compon,
## dataset} => {experi} 0.1000000 1.0000000 3.750000 3
## [5907] {model,
## dataset,
## experi} => {compon} 0.1000000 1.0000000 10.000000 3
## [5908] {compon,
## experi,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [5909] {model,
## compon,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [5910] {model,
## compon,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [5911] {model,
## experi,
## propos} => {compon} 0.1000000 1.0000000 10.000000 3
## [5912] {compon,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [5913] {model,
## compon,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [5914] {model,
## compon,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5915] {train,
## neural,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [5916] {neural,
## provid,
## work} => {train} 0.1000000 1.0000000 2.500000 3
## [5917] {train,
## provid,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [5918] {train,
## neural,
## work} => {provid} 0.1000000 1.0000000 10.000000 3
## [5919] {train,
## neural,
## provid} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5920] {dataset,
## neural,
## provid} => {train} 0.1000000 1.0000000 2.500000 3
## [5921] {train,
## dataset,
## provid} => {neural} 0.1000000 1.0000000 3.000000 3
## [5922] {train,
## neural,
## provid} => {network} 0.1000000 1.0000000 1.578947 3
## [5923] {network,
## neural,
## provid} => {train} 0.1000000 1.0000000 2.500000 3
## [5924] {network,
## train,
## provid} => {neural} 0.1000000 1.0000000 3.000000 3
## [5925] {neural,
## provid,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5926] {dataset,
## neural,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [5927] {dataset,
## provid,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [5928] {dataset,
## neural,
## work} => {provid} 0.1000000 1.0000000 10.000000 3
## [5929] {neural,
## provid,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [5930] {network,
## neural,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [5931] {network,
## provid,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [5932] {dataset,
## neural,
## provid} => {network} 0.1000000 1.0000000 1.578947 3
## [5933] {network,
## neural,
## provid} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5934] {network,
## dataset,
## provid} => {neural} 0.1000000 1.0000000 3.000000 3
## [5935] {train,
## provid,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5936] {train,
## dataset,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [5937] {dataset,
## provid,
## work} => {train} 0.1000000 1.0000000 2.500000 3
## [5938] {train,
## dataset,
## work} => {provid} 0.1000000 1.0000000 10.000000 3
## [5939] {train,
## provid,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [5940] {network,
## train,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [5941] {network,
## provid,
## work} => {train} 0.1000000 1.0000000 2.500000 3
## [5942] {network,
## train,
## work} => {provid} 0.1000000 0.7500000 7.500000 3
## [5943] {train,
## dataset,
## provid} => {network} 0.1000000 1.0000000 1.578947 3
## [5944] {network,
## train,
## provid} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5945] {network,
## dataset,
## provid} => {train} 0.1000000 1.0000000 2.500000 3
## [5946] {dataset,
## provid,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [5947] {network,
## provid,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [5948] {network,
## dataset,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [5949] {classif,
## object,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [5950] {object,
## propos,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [5951] {classif,
## propos,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [5952] {classif,
## object,
## propos} => {test} 0.1000000 0.7500000 7.500000 3
## [5953] {classif,
## object,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [5954] {model,
## object,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [5955] {classif,
## model,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [5956] {classif,
## model,
## object} => {test} 0.1000000 1.0000000 10.000000 3
## [5957] {classif,
## object,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [5958] {featur,
## object,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [5959] {classif,
## featur,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [5960] {classif,
## featur,
## object} => {test} 0.1000000 0.7500000 7.500000 3
## [5961] {object,
## propos,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [5962] {model,
## object,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [5963] {model,
## propos,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [5964] {model,
## object,
## propos} => {test} 0.1000000 0.7500000 7.500000 3
## [5965] {object,
## propos,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [5966] {featur,
## object,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [5967] {featur,
## propos,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [5968] {model,
## object,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [5969] {featur,
## object,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [5970] {featur,
## model,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [5971] {featur,
## model,
## object} => {test} 0.1000000 0.7500000 7.500000 3
## [5972] {classif,
## propos,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [5973] {classif,
## model,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [5974] {model,
## propos,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [5975] {classif,
## model,
## propos} => {test} 0.1000000 1.0000000 10.000000 3
## [5976] {classif,
## propos,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [5977] {classif,
## featur,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [5978] {featur,
## propos,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [5979] {classif,
## model,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [5980] {classif,
## featur,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [5981] {featur,
## model,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [5982] {model,
## propos,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [5983] {featur,
## propos,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [5984] {featur,
## model,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [5985] {extract,
## general,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [5986] {extract,
## general,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [5987] {extract,
## recognit,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [5988] {general,
## recognit,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [5989] {extract,
## general,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [5990] {show,
## extract,
## general} => {recognit} 0.1000000 1.0000000 3.333333 3
## [5991] {show,
## extract,
## recognit} => {general} 0.1000000 1.0000000 5.000000 3
## [5992] {show,
## general,
## recognit} => {extract} 0.1000000 0.7500000 5.625000 3
## [5993] {extract,
## general,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [5994] {featur,
## extract,
## general} => {recognit} 0.1000000 1.0000000 3.333333 3
## [5995] {featur,
## extract,
## recognit} => {general} 0.1000000 1.0000000 5.000000 3
## [5996] {featur,
## general,
## recognit} => {extract} 0.1000000 1.0000000 7.500000 3
## [5997] {extract,
## general,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [5998] {show,
## extract,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [5999] {show,
## extract,
## result} => {general} 0.1000000 0.7500000 3.750000 3
## [6000] {show,
## general,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6001] {extract,
## general,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [6002] {featur,
## extract,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [6003] {featur,
## extract,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [6004] {featur,
## general,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6005] {show,
## extract,
## general} => {featur} 0.1000000 1.0000000 1.875000 3
## [6006] {featur,
## extract,
## general} => {show} 0.1000000 1.0000000 1.875000 3
## [6007] {featur,
## show,
## extract} => {general} 0.1000000 1.0000000 5.000000 3
## [6008] {featur,
## show,
## general} => {extract} 0.1000000 1.0000000 7.500000 3
## [6009] {extract,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [6010] {show,
## extract,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [6011] {show,
## extract,
## result} => {recognit} 0.1000000 0.7500000 2.500000 3
## [6012] {show,
## recognit,
## result} => {extract} 0.1000000 0.7500000 5.625000 3
## [6013] {extract,
## recognit,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [6014] {featur,
## extract,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [6015] {featur,
## extract,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6016] {featur,
## recognit,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6017] {show,
## extract,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [6018] {featur,
## extract,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [6019] {featur,
## show,
## extract} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6020] {featur,
## show,
## recognit} => {extract} 0.1000000 0.7500000 5.625000 3
## [6021] {algorithm,
## extract,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [6022] {show,
## extract,
## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6023] {show,
## algorithm,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [6024] {show,
## algorithm,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6025] {algorithm,
## extract,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [6026] {model,
## extract,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6027] {model,
## algorithm,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [6028] {model,
## algorithm,
## result} => {extract} 0.1000000 0.7500000 5.625000 3
## [6029] {data,
## extract,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [6030] {show,
## extract,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [6031] {data,
## show,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [6032] {data,
## show,
## result} => {extract} 0.1000000 0.7500000 5.625000 3
## [6033] {show,
## extract,
## result} => {model} 0.1000000 0.7500000 1.406250 3
## [6034] {model,
## extract,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [6035] {model,
## show,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [6036] {model,
## show,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6037] {show,
## extract,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [6038] {featur,
## extract,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [6039] {featur,
## show,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [6040] {featur,
## show,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6041] {show,
## algorithm,
## extract} => {model} 0.1000000 1.0000000 1.875000 3
## [6042] {model,
## algorithm,
## extract} => {show} 0.1000000 1.0000000 1.875000 3
## [6043] {model,
## show,
## extract} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6044] {appli,
## dataset,
## report} => {propos} 0.1000000 1.0000000 2.000000 3
## [6045] {appli,
## propos,
## report} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6046] {dataset,
## propos,
## report} => {appli} 0.1000000 1.0000000 5.000000 3
## [6047] {appli,
## dataset,
## propos} => {report} 0.1000000 0.7500000 5.625000 3
## [6048] {appli,
## dataset,
## report} => {network} 0.1000000 1.0000000 1.578947 3
## [6049] {network,
## appli,
## report} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6050] {network,
## dataset,
## report} => {appli} 0.1000000 1.0000000 5.000000 3
## [6051] {network,
## appli,
## dataset} => {report} 0.1000000 0.7500000 5.625000 3
## [6052] {appli,
## propos,
## report} => {network} 0.1000000 1.0000000 1.578947 3
## [6053] {network,
## appli,
## report} => {propos} 0.1000000 1.0000000 2.000000 3
## [6054] {network,
## propos,
## report} => {appli} 0.1000000 1.0000000 5.000000 3
## [6055] {network,
## appli,
## propos} => {report} 0.1000000 0.7500000 5.625000 3
## [6056] {dataset,
## propos,
## report} => {network} 0.1000000 1.0000000 1.578947 3
## [6057] {network,
## dataset,
## report} => {propos} 0.1000000 1.0000000 2.000000 3
## [6058] {network,
## propos,
## report} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6059] {attribut,
## outperform,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [6060] {attribut,
## outperform,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [6061] {attribut,
## represent,
## show} => {outperform} 0.1000000 1.0000000 7.500000 3
## [6062] {outperform,
## represent,
## show} => {attribut} 0.1000000 1.0000000 10.000000 3
## [6063] {data,
## employ,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6064] {employ,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6065] {data,
## employ,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [6066] {data,
## employ,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6067] {employ,
## featur,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6068] {data,
## employ,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [6069] {employ,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6070] {employ,
## featur,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6071] {employ,
## featur,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [6072] {data,
## employ,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [6073] {data,
## employ,
## featur} => {model} 0.1000000 1.0000000 1.875000 3
## [6074] {employ,
## featur,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [6075] {approach,
## achiev,
## abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [6076] {achiev,
## propos,
## abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [6077] {approach,
## propos,
## abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [6078] {approach,
## achiev,
## propos} => {abil} 0.1000000 0.7500000 7.500000 3
## [6079] {approach,
## achiev,
## abil} => {network} 0.1000000 1.0000000 1.578947 3
## [6080] {network,
## achiev,
## abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [6081] {approach,
## network,
## abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [6082] {approach,
## network,
## achiev} => {abil} 0.1000000 0.7500000 7.500000 3
## [6083] {achiev,
## propos,
## abil} => {network} 0.1000000 1.0000000 1.578947 3
## [6084] {network,
## achiev,
## abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [6085] {network,
## propos,
## abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [6086] {network,
## achiev,
## propos} => {abil} 0.1000000 0.7500000 7.500000 3
## [6087] {approach,
## propos,
## abil} => {network} 0.1000000 1.0000000 1.578947 3
## [6088] {approach,
## network,
## abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [6089] {network,
## propos,
## abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [6090] {data,
## algorithm,
## structur} => {show} 0.1000000 1.0000000 1.875000 3
## [6091] {show,
## algorithm,
## structur} => {data} 0.1000000 1.0000000 2.307692 3
## [6092] {data,
## show,
## structur} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6093] {data,
## show,
## algorithm} => {structur} 0.1000000 1.0000000 7.500000 3
## [6094] {data,
## algorithm,
## structur} => {model} 0.1000000 1.0000000 1.875000 3
## [6095] {model,
## algorithm,
## structur} => {data} 0.1000000 1.0000000 2.307692 3
## [6096] {data,
## model,
## structur} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6097] {data,
## model,
## algorithm} => {structur} 0.1000000 1.0000000 7.500000 3
## [6098] {show,
## algorithm,
## structur} => {model} 0.1000000 1.0000000 1.875000 3
## [6099] {model,
## algorithm,
## structur} => {show} 0.1000000 1.0000000 1.875000 3
## [6100] {model,
## show,
## structur} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6101] {data,
## show,
## structur} => {model} 0.1000000 1.0000000 1.875000 3
## [6102] {data,
## model,
## structur} => {show} 0.1000000 1.0000000 1.875000 3
## [6103] {model,
## show,
## structur} => {data} 0.1000000 1.0000000 2.307692 3
## [6104] {show,
## learn,
## structur} => {featur} 0.1000000 1.0000000 1.875000 3
## [6105] {featur,
## learn,
## structur} => {show} 0.1000000 1.0000000 1.875000 3
## [6106] {featur,
## show,
## structur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6107] {show,
## learn,
## inform} => {propos} 0.1000000 1.0000000 2.000000 3
## [6108] {propos,
## learn,
## inform} => {show} 0.1000000 1.0000000 1.875000 3
## [6109] {show,
## propos,
## inform} => {learn} 0.1000000 1.0000000 2.307692 3
## [6110] {show,
## learn,
## inform} => {model} 0.1000000 1.0000000 1.875000 3
## [6111] {model,
## learn,
## inform} => {show} 0.1000000 1.0000000 1.875000 3
## [6112] {model,
## show,
## inform} => {learn} 0.1000000 1.0000000 2.307692 3
## [6113] {propos,
## learn,
## inform} => {model} 0.1000000 1.0000000 1.875000 3
## [6114] {model,
## learn,
## inform} => {propos} 0.1000000 1.0000000 2.000000 3
## [6115] {model,
## propos,
## inform} => {learn} 0.1000000 1.0000000 2.307692 3
## [6116] {show,
## propos,
## inform} => {model} 0.1000000 1.0000000 1.875000 3
## [6117] {model,
## show,
## inform} => {propos} 0.1000000 1.0000000 2.000000 3
## [6118] {model,
## propos,
## inform} => {show} 0.1000000 1.0000000 1.875000 3
## [6119] {model,
## show,
## propos} => {inform} 0.1000000 0.7500000 7.500000 3
## [6120] {architectur,
## convolut,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [6121] {convolut,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [6122] {architectur,
## convolut,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [6123] {architectur,
## effici,
## work} => {convolut} 0.1000000 0.7500000 5.625000 3
## [6124] {architectur,
## convolut,
## effici} => {perform} 0.1000000 1.0000000 2.142857 3
## [6125] {perform,
## convolut,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [6126] {architectur,
## perform,
## convolut} => {effici} 0.1000000 1.0000000 6.000000 3
## [6127] {architectur,
## perform,
## effici} => {convolut} 0.1000000 1.0000000 7.500000 3
## [6128] {architectur,
## convolut,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [6129] {network,
## convolut,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [6130] {network,
## architectur,
## convolut} => {effici} 0.1000000 1.0000000 6.000000 3
## [6131] {network,
## architectur,
## effici} => {convolut} 0.1000000 0.7500000 5.625000 3
## [6132] {convolut,
## effici,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [6133] {perform,
## convolut,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [6134] {perform,
## convolut,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [6135] {perform,
## effici,
## work} => {convolut} 0.1000000 1.0000000 7.500000 3
## [6136] {convolut,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [6137] {network,
## convolut,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [6138] {network,
## convolut,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [6139] {network,
## effici,
## work} => {convolut} 0.1000000 0.7500000 5.625000 3
## [6140] {perform,
## convolut,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [6141] {network,
## convolut,
## effici} => {perform} 0.1000000 1.0000000 2.142857 3
## [6142] {network,
## perform,
## convolut} => {effici} 0.1000000 1.0000000 6.000000 3
## [6143] {network,
## perform,
## effici} => {convolut} 0.1000000 1.0000000 7.500000 3
## [6144] {architectur,
## convolut,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [6145] {architectur,
## perform,
## convolut} => {work} 0.1000000 1.0000000 2.500000 3
## [6146] {perform,
## convolut,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [6147] {architectur,
## perform,
## work} => {convolut} 0.1000000 0.7500000 5.625000 3
## [6148] {architectur,
## convolut,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [6149] {network,
## architectur,
## convolut} => {work} 0.1000000 1.0000000 2.500000 3
## [6150] {network,
## convolut,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [6151] {architectur,
## perform,
## convolut} => {network} 0.1000000 1.0000000 1.578947 3
## [6152] {network,
## architectur,
## convolut} => {perform} 0.1000000 1.0000000 2.142857 3
## [6153] {network,
## perform,
## convolut} => {architectur} 0.1000000 1.0000000 3.750000 3
## [6154] {perform,
## convolut,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [6155] {network,
## convolut,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [6156] {network,
## perform,
## convolut} => {work} 0.1000000 1.0000000 2.500000 3
## [6157] {network,
## perform,
## work} => {convolut} 0.1000000 0.7500000 5.625000 3
## [6158] {work,
## larg,
## highlevel} => {represent} 0.1000000 1.0000000 2.000000 3
## [6159] {represent,
## larg,
## highlevel} => {work} 0.1000000 1.0000000 2.500000 3
## [6160] {represent,
## work,
## highlevel} => {larg} 0.1000000 1.0000000 6.000000 3
## [6161] {represent,
## work,
## larg} => {highlevel} 0.1000000 0.7500000 7.500000 3
## [6162] {task,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [6163] {data,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [6164] {data,
## task,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6165] {data,
## task,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6166] {task,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6167] {dataset,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [6168] {task,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6169] {task,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6170] {task,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [6171] {learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [6172] {task,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6173] {task,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6174] {task,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [6175] {featur,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [6176] {featur,
## task,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6177] {featur,
## task,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6178] {task,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [6179] {network,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [6180] {network,
## task,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6181] {network,
## task,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6182] {data,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6183] {dataset,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [6184] {data,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6185] {data,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6186] {data,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [6187] {learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [6188] {data,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6189] {data,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6190] {data,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [6191] {featur,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [6192] {data,
## featur,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6193] {data,
## featur,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6194] {data,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [6195] {network,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [6196] {data,
## network,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6197] {data,
## network,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6198] {dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [6199] {learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6200] {dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6201] {dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6202] {dataset,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [6203] {featur,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6204] {featur,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6205] {featur,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6206] {dataset,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [6207] {network,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6208] {network,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6209] {network,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6210] {learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [6211] {featur,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [6212] {featur,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6213] {featur,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6214] {learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [6215] {network,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [6216] {network,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6217] {network,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6218] {featur,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [6219] {network,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [6220] {featur,
## network,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [6221] {featur,
## network,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [6222] {data,
## task,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6223] {task,
## dataset,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [6224] {data,
## dataset,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [6225] {data,
## task,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [6226] {task,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [6227] {data,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [6228] {data,
## task,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [6229] {featur,
## task,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [6230] {data,
## featur,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [6231] {data,
## task,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [6232] {network,
## task,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [6233] {data,
## network,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [6234] {task,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [6235] {task,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6236] {dataset,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [6237] {task,
## dataset,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [6238] {featur,
## task,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6239] {featur,
## dataset,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [6240] {featur,
## task,
## dataset} => {care} 0.1000000 0.7500000 7.500000 3
## [6241] {task,
## dataset,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [6242] {network,
## task,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6243] {network,
## dataset,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [6244] {network,
## task,
## dataset} => {care} 0.1000000 0.7500000 7.500000 3
## [6245] {task,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [6246] {featur,
## task,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [6247] {featur,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [6248] {task,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [6249] {network,
## task,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [6250] {network,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [6251] {featur,
## task,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [6252] {network,
## task,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [6253] {featur,
## network,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [6254] {data,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [6255] {data,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6256] {dataset,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [6257] {data,
## dataset,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [6258] {data,
## featur,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6259] {featur,
## dataset,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [6260] {data,
## dataset,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [6261] {data,
## network,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6262] {network,
## dataset,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [6263] {data,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [6264] {data,
## featur,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [6265] {featur,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [6266] {data,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [6267] {data,
## network,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [6268] {network,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [6269] {data,
## network,
## learn} => {care} 0.1000000 0.7500000 7.500000 3
## [6270] {data,
## featur,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [6271] {data,
## network,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [6272] {featur,
## network,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [6273] {dataset,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [6274] {featur,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [6275] {featur,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6276] {dataset,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [6277] {network,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [6278] {network,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6279] {network,
## dataset,
## learn} => {care} 0.1000000 0.7500000 7.500000 3
## [6280] {featur,
## dataset,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [6281] {network,
## dataset,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [6282] {featur,
## network,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6283] {featur,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [6284] {network,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [6285] {featur,
## network,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [6286] {dataset,
## learn,
## rate} => {represent} 0.1000000 1.0000000 2.000000 3
## [6287] {represent,
## dataset,
## rate} => {learn} 0.1000000 1.0000000 2.307692 3
## [6288] {represent,
## learn,
## rate} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6289] {dataset,
## learn,
## rate} => {show} 0.1000000 1.0000000 1.875000 3
## [6290] {show,
## dataset,
## rate} => {learn} 0.1000000 1.0000000 2.307692 3
## [6291] {show,
## learn,
## rate} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6292] {represent,
## dataset,
## rate} => {show} 0.1000000 1.0000000 1.875000 3
## [6293] {show,
## dataset,
## rate} => {represent} 0.1000000 1.0000000 2.000000 3
## [6294] {represent,
## show,
## rate} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6295] {represent,
## show,
## dataset} => {rate} 0.1000000 0.7500000 5.625000 3
## [6296] {represent,
## learn,
## rate} => {show} 0.1000000 1.0000000 1.875000 3
## [6297] {show,
## learn,
## rate} => {represent} 0.1000000 1.0000000 2.000000 3
## [6298] {represent,
## show,
## rate} => {learn} 0.1000000 1.0000000 2.307692 3
## [6299] {approach,
## captur,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [6300] {approach,
## represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6301] {represent,
## captur,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [6302] {approach,
## captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [6303] {approach,
## featur,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6304] {featur,
## captur,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [6305] {approach,
## captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [6306] {approach,
## network,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6307] {network,
## captur,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [6308] {approach,
## network,
## learn} => {captur} 0.1000000 0.7500000 7.500000 3
## [6309] {approach,
## represent,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [6310] {approach,
## featur,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [6311] {featur,
## represent,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [6312] {approach,
## represent,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [6313] {approach,
## network,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [6314] {network,
## represent,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [6315] {approach,
## featur,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [6316] {approach,
## network,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [6317] {featur,
## network,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [6318] {represent,
## captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [6319] {featur,
## captur,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [6320] {featur,
## represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6321] {represent,
## captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [6322] {network,
## captur,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [6323] {network,
## represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6324] {featur,
## captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [6325] {network,
## captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [6326] {featur,
## network,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6327] {featur,
## represent,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [6328] {network,
## represent,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [6329] {featur,
## network,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [6330] {approach,
## dataset,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [6331] {approach,
## propos,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6332] {dataset,
## propos,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [6333] {approach,
## dataset,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [6334] {approach,
## network,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6335] {network,
## dataset,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [6336] {approach,
## propos,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [6337] {approach,
## network,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [6338] {network,
## propos,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [6339] {dataset,
## propos,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [6340] {network,
## dataset,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [6341] {network,
## propos,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6342] {process,
## simpl,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [6343] {simpl,
## studi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [6344] {process,
## simpl,
## work} => {studi} 0.1000000 1.0000000 7.500000 3
## [6345] {process,
## studi,
## work} => {simpl} 0.1000000 1.0000000 10.000000 3
## [6346] {process,
## simpl,
## studi} => {network} 0.1000000 1.0000000 1.578947 3
## [6347] {network,
## simpl,
## studi} => {process} 0.1000000 1.0000000 5.000000 3
## [6348] {network,
## process,
## simpl} => {studi} 0.1000000 1.0000000 7.500000 3
## [6349] {network,
## process,
## studi} => {simpl} 0.1000000 1.0000000 10.000000 3
## [6350] {simpl,
## studi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [6351] {network,
## simpl,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [6352] {network,
## simpl,
## work} => {studi} 0.1000000 1.0000000 7.500000 3
## [6353] {network,
## studi,
## work} => {simpl} 0.1000000 1.0000000 10.000000 3
## [6354] {process,
## simpl,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [6355] {network,
## process,
## simpl} => {work} 0.1000000 1.0000000 2.500000 3
## [6356] {network,
## simpl,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [6357] {network,
## process,
## work} => {simpl} 0.1000000 0.7500000 7.500000 3
## [6358] {train,
## result,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6359] {dataset,
## result,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [6360] {train,
## dataset,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [6361] {train,
## dataset,
## result} => {increas} 0.1000000 0.7500000 5.625000 3
## [6362] {train,
## result,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [6363] {network,
## result,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [6364] {network,
## train,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [6365] {dataset,
## result,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [6366] {network,
## result,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6367] {network,
## dataset,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [6368] {algorithm,
## neural,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [6369] {network,
## neural,
## increas} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6370] {network,
## algorithm,
## increas} => {neural} 0.1000000 1.0000000 3.000000 3
## [6371] {train,
## dataset,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [6372] {network,
## train,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6373] {network,
## dataset,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [6374] {featur,
## represent,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [6375] {network,
## represent,
## increas} => {featur} 0.1000000 1.0000000 1.875000 3
## [6376] {featur,
## network,
## increas} => {represent} 0.1000000 1.0000000 2.000000 3
## [6377] {boltzmann,
## restrict,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6378] {boltzmann,
## machin,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [6379] {boltzmann,
## machin,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6380] {machin,
## restrict,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6381] {boltzmann,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6382] {boltzmann,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6383] {boltzmann,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6384] {restrict,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6385] {boltzmann,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6386] {boltzmann,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [6387] {boltzmann,
## show,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6388] {restrict,
## show,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6389] {boltzmann,
## restrict,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6390] {boltzmann,
## model,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [6391] {boltzmann,
## model,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6392] {model,
## restrict,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6393] {boltzmann,
## machin,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6394] {boltzmann,
## restrict,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [6395] {boltzmann,
## machin,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6396] {machin,
## restrict,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6397] {boltzmann,
## machin,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [6398] {boltzmann,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6399] {boltzmann,
## machin,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6400] {machin,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6401] {boltzmann,
## machin,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [6402] {boltzmann,
## restrict,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [6403] {boltzmann,
## machin,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6404] {machin,
## restrict,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6405] {boltzmann,
## machin,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [6406] {boltzmann,
## data,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [6407] {boltzmann,
## data,
## machin} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6408] {data,
## machin,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6409] {boltzmann,
## machin,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [6410] {boltzmann,
## restrict,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [6411] {boltzmann,
## machin,
## show} => {restrict} 0.1333333 1.0000000 7.500000 4
## [6412] {machin,
## restrict,
## show} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [6413] {boltzmann,
## machin,
## restrict} => {model} 0.1333333 1.0000000 1.875000 4
## [6414] {boltzmann,
## model,
## restrict} => {machin} 0.1333333 1.0000000 4.285714 4
## [6415] {boltzmann,
## machin,
## model} => {restrict} 0.1333333 1.0000000 7.500000 4
## [6416] {machin,
## model,
## restrict} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [6417] {boltzmann,
## machin,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [6418] {boltzmann,
## featur,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [6419] {boltzmann,
## featur,
## machin} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6420] {featur,
## machin,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6421] {boltzmann,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [6422] {boltzmann,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6423] {boltzmann,
## show,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6424] {restrict,
## show,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6425] {boltzmann,
## restrict,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [6426] {boltzmann,
## model,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6427] {boltzmann,
## model,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6428] {model,
## restrict,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6429] {boltzmann,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6430] {boltzmann,
## data,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6431] {boltzmann,
## data,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6432] {data,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6433] {boltzmann,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6434] {boltzmann,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [6435] {boltzmann,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6436] {restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6437] {boltzmann,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6438] {boltzmann,
## model,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [6439] {boltzmann,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6440] {model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6441] {boltzmann,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6442] {boltzmann,
## featur,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6443] {boltzmann,
## featur,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6444] {featur,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6445] {boltzmann,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6446] {boltzmann,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [6447] {boltzmann,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6448] {restrict,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6449] {boltzmann,
## restrict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6450] {boltzmann,
## model,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [6451] {boltzmann,
## model,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6452] {model,
## restrict,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6453] {boltzmann,
## data,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6454] {boltzmann,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [6455] {boltzmann,
## data,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6456] {data,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6457] {boltzmann,
## data,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [6458] {boltzmann,
## model,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [6459] {boltzmann,
## data,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6460] {data,
## model,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6461] {boltzmann,
## data,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6462] {boltzmann,
## featur,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [6463] {boltzmann,
## data,
## featur} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6464] {data,
## featur,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6465] {boltzmann,
## restrict,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [6466] {boltzmann,
## model,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [6467] {boltzmann,
## model,
## show} => {restrict} 0.1333333 1.0000000 7.500000 4
## [6468] {model,
## restrict,
## show} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [6469] {boltzmann,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [6470] {boltzmann,
## featur,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6471] {boltzmann,
## featur,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6472] {featur,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6473] {boltzmann,
## model,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [6474] {boltzmann,
## featur,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [6475] {boltzmann,
## featur,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6476] {featur,
## model,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6477] {boltzmann,
## machin,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6478] {boltzmann,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6479] {boltzmann,
## machin,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6480] {machin,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6481] {boltzmann,
## machin,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6482] {boltzmann,
## show,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6483] {boltzmann,
## machin,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [6484] {machin,
## show,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6485] {boltzmann,
## machin,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6486] {boltzmann,
## model,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6487] {boltzmann,
## machin,
## model} => {recent} 0.1000000 0.7500000 3.214286 3
## [6488] {machin,
## model,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6489] {boltzmann,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6490] {boltzmann,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6491] {boltzmann,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6492] {show,
## algorithm,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6493] {boltzmann,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6494] {boltzmann,
## model,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6495] {boltzmann,
## model,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6496] {model,
## algorithm,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6497] {boltzmann,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6498] {boltzmann,
## model,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6499] {boltzmann,
## model,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [6500] {boltzmann,
## machin,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [6501] {boltzmann,
## machin,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6502] {boltzmann,
## show,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [6503] {machin,
## show,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6504] {boltzmann,
## machin,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [6505] {boltzmann,
## machin,
## model} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6506] {boltzmann,
## model,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [6507] {machin,
## model,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6508] {boltzmann,
## machin,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6509] {boltzmann,
## data,
## machin} => {task} 0.1000000 1.0000000 2.727273 3
## [6510] {boltzmann,
## data,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6511] {data,
## machin,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6512] {boltzmann,
## machin,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6513] {boltzmann,
## machin,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [6514] {boltzmann,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6515] {machin,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6516] {boltzmann,
## machin,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6517] {boltzmann,
## machin,
## model} => {task} 0.1000000 0.7500000 2.045455 3
## [6518] {boltzmann,
## model,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6519] {machin,
## model,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6520] {boltzmann,
## machin,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6521] {boltzmann,
## featur,
## machin} => {task} 0.1000000 1.0000000 2.727273 3
## [6522] {boltzmann,
## featur,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6523] {featur,
## machin,
## task} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6524] {boltzmann,
## machin,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6525] {boltzmann,
## machin,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [6526] {boltzmann,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [6527] {machin,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6528] {boltzmann,
## machin,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6529] {boltzmann,
## machin,
## model} => {train} 0.1000000 0.7500000 1.875000 3
## [6530] {boltzmann,
## model,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [6531] {machin,
## model,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6532] {boltzmann,
## data,
## machin} => {show} 0.1000000 1.0000000 1.875000 3
## [6533] {boltzmann,
## machin,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [6534] {boltzmann,
## data,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [6535] {data,
## machin,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6536] {boltzmann,
## data,
## machin} => {model} 0.1000000 1.0000000 1.875000 3
## [6537] {boltzmann,
## machin,
## model} => {data} 0.1000000 0.7500000 1.730769 3
## [6538] {boltzmann,
## data,
## model} => {machin} 0.1000000 1.0000000 4.285714 3
## [6539] {data,
## machin,
## model} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6540] {boltzmann,
## data,
## machin} => {featur} 0.1000000 1.0000000 1.875000 3
## [6541] {boltzmann,
## featur,
## machin} => {data} 0.1000000 1.0000000 2.307692 3
## [6542] {boltzmann,
## data,
## featur} => {machin} 0.1000000 1.0000000 4.285714 3
## [6543] {data,
## featur,
## machin} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6544] {boltzmann,
## machin,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [6545] {boltzmann,
## machin,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [6546] {boltzmann,
## model,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [6547] {machin,
## model,
## show} => {boltzmann} 0.1333333 0.8000000 6.000000 4
## [6548] {boltzmann,
## machin,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [6549] {boltzmann,
## featur,
## machin} => {show} 0.1000000 1.0000000 1.875000 3
## [6550] {boltzmann,
## featur,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [6551] {featur,
## machin,
## show} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6552] {boltzmann,
## machin,
## model} => {featur} 0.1000000 0.7500000 1.406250 3
## [6553] {boltzmann,
## featur,
## machin} => {model} 0.1000000 1.0000000 1.875000 3
## [6554] {boltzmann,
## featur,
## model} => {machin} 0.1000000 1.0000000 4.285714 3
## [6555] {featur,
## machin,
## model} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6556] {boltzmann,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [6557] {boltzmann,
## model,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [6558] {boltzmann,
## model,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6559] {boltzmann,
## data,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6560] {boltzmann,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6561] {boltzmann,
## data,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [6562] {boltzmann,
## data,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6563] {boltzmann,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6564] {boltzmann,
## data,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [6565] {boltzmann,
## data,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6566] {boltzmann,
## featur,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6567] {boltzmann,
## data,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [6568] {boltzmann,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6569] {boltzmann,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6570] {boltzmann,
## model,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [6571] {model,
## show,
## task} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6572] {boltzmann,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6573] {boltzmann,
## featur,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6574] {boltzmann,
## featur,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [6575] {boltzmann,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6576] {boltzmann,
## featur,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6577] {boltzmann,
## featur,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [6578] {boltzmann,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6579] {boltzmann,
## model,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6580] {boltzmann,
## model,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [6581] {model,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6582] {boltzmann,
## data,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [6583] {boltzmann,
## data,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [6584] {boltzmann,
## model,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [6585] {boltzmann,
## data,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [6586] {boltzmann,
## data,
## featur} => {show} 0.1000000 1.0000000 1.875000 3
## [6587] {boltzmann,
## featur,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [6588] {boltzmann,
## data,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [6589] {boltzmann,
## data,
## featur} => {model} 0.1000000 1.0000000 1.875000 3
## [6590] {boltzmann,
## featur,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [6591] {boltzmann,
## model,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [6592] {boltzmann,
## featur,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [6593] {boltzmann,
## featur,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [6594] {machin,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6595] {restrict,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6596] {machin,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6597] {machin,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6598] {machin,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6599] {restrict,
## show,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6600] {machin,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [6601] {machin,
## show,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6602] {machin,
## restrict,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6603] {model,
## restrict,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6604] {machin,
## model,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [6605] {machin,
## model,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6606] {restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6607] {restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6608] {restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6609] {show,
## algorithm,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6610] {restrict,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6611] {model,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6612] {model,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6613] {model,
## algorithm,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6614] {restrict,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6615] {model,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6616] {model,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [6617] {machin,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [6618] {machin,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6619] {restrict,
## show,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [6620] {machin,
## show,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6621] {machin,
## restrict,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [6622] {machin,
## model,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6623] {model,
## restrict,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [6624] {machin,
## model,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6625] {machin,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6626] {data,
## machin,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6627] {data,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6628] {data,
## machin,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6629] {machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6630] {machin,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [6631] {restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6632] {machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6633] {machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6634] {machin,
## model,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [6635] {model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6636] {machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6637] {machin,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6638] {featur,
## machin,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6639] {featur,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6640] {featur,
## machin,
## task} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6641] {machin,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6642] {machin,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [6643] {restrict,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [6644] {machin,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6645] {machin,
## restrict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6646] {machin,
## model,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [6647] {model,
## restrict,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [6648] {machin,
## model,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6649] {data,
## machin,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6650] {machin,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [6651] {data,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [6652] {data,
## machin,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6653] {data,
## machin,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [6654] {machin,
## model,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [6655] {data,
## model,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [6656] {data,
## machin,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6657] {data,
## machin,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6658] {featur,
## machin,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [6659] {data,
## featur,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [6660] {data,
## featur,
## machin} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6661] {machin,
## restrict,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [6662] {machin,
## model,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [6663] {model,
## restrict,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [6664] {machin,
## model,
## show} => {restrict} 0.1333333 0.8000000 6.000000 4
## [6665] {machin,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [6666] {featur,
## machin,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6667] {featur,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [6668] {featur,
## machin,
## show} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6669] {machin,
## model,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [6670] {featur,
## machin,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [6671] {featur,
## model,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [6672] {featur,
## machin,
## model} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6673] {restrict,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [6674] {model,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [6675] {model,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6676] {data,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6677] {restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6678] {data,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [6679] {data,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6680] {model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6681] {data,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6682] {data,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6683] {featur,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6684] {data,
## featur,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6685] {restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6686] {model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6687] {model,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [6688] {model,
## show,
## task} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6689] {restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6690] {featur,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6691] {featur,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [6692] {model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6693] {featur,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6694] {featur,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6695] {restrict,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6696] {model,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6697] {model,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [6698] {model,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6699] {data,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [6700] {data,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6701] {model,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [6702] {data,
## restrict,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [6703] {data,
## featur,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6704] {featur,
## restrict,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [6705] {data,
## model,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6706] {data,
## featur,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [6707] {featur,
## model,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [6708] {model,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [6709] {featur,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [6710] {featur,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6711] {dataset,
## learn,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [6712] {dataset,
## propos,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [6713] {propos,
## learn,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6714] {dataset,
## learn,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [6715] {model,
## dataset,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [6716] {model,
## learn,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6717] {dataset,
## propos,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [6718] {model,
## dataset,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [6719] {model,
## propos,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6720] {propos,
## learn,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [6721] {model,
## learn,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [6722] {model,
## propos,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [6723] {algorithm,
## neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [6724] {approach,
## neural,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6725] {approach,
## algorithm,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [6726] {approach,
## algorithm,
## neural} => {order} 0.1000000 0.7500000 5.625000 3
## [6727] {algorithm,
## neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6728] {model,
## neural,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6729] {model,
## algorithm,
## order} => {neural} 0.1000000 0.7500000 2.250000 3
## [6730] {model,
## algorithm,
## neural} => {order} 0.1000000 1.0000000 7.500000 3
## [6731] {algorithm,
## neural,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [6732] {network,
## neural,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6733] {network,
## algorithm,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [6734] {approach,
## neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6735] {model,
## neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [6736] {approach,
## model,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [6737] {approach,
## model,
## neural} => {order} 0.1000000 1.0000000 7.500000 3
## [6738] {approach,
## neural,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [6739] {network,
## neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [6740] {approach,
## network,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [6741] {model,
## neural,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [6742] {network,
## neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6743] {model,
## network,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [6744] {model,
## network,
## neural} => {order} 0.1000000 0.7500000 5.625000 3
## [6745] {approach,
## algorithm,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6746] {model,
## algorithm,
## order} => {approach} 0.1000000 0.7500000 1.875000 3
## [6747] {approach,
## model,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6748] {approach,
## model,
## algorithm} => {order} 0.1000000 1.0000000 7.500000 3
## [6749] {approach,
## algorithm,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [6750] {network,
## algorithm,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [6751] {approach,
## network,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6752] {approach,
## network,
## algorithm} => {order} 0.1000000 0.7500000 5.625000 3
## [6753] {show,
## algorithm,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6754] {model,
## algorithm,
## order} => {show} 0.1000000 0.7500000 1.406250 3
## [6755] {model,
## show,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6756] {show,
## algorithm,
## order} => {featur} 0.1000000 1.0000000 1.875000 3
## [6757] {featur,
## algorithm,
## order} => {show} 0.1000000 1.0000000 1.875000 3
## [6758] {featur,
## show,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6759] {featur,
## show,
## algorithm} => {order} 0.1000000 0.7500000 5.625000 3
## [6760] {algorithm,
## order,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [6761] {model,
## algorithm,
## order} => {propos} 0.1000000 0.7500000 1.500000 3
## [6762] {model,
## order,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6763] {model,
## algorithm,
## propos} => {order} 0.1000000 1.0000000 7.500000 3
## [6764] {model,
## algorithm,
## order} => {featur} 0.1000000 0.7500000 1.406250 3
## [6765] {featur,
## algorithm,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6766] {featur,
## model,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6767] {featur,
## model,
## algorithm} => {order} 0.1000000 0.7500000 5.625000 3
## [6768] {model,
## algorithm,
## order} => {network} 0.1000000 0.7500000 1.184211 3
## [6769] {network,
## algorithm,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6770] {model,
## network,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6771] {model,
## network,
## algorithm} => {order} 0.1000000 1.0000000 7.500000 3
## [6772] {approach,
## model,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [6773] {approach,
## network,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6774] {model,
## network,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [6775] {model,
## show,
## order} => {featur} 0.1000000 1.0000000 1.875000 3
## [6776] {featur,
## show,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6777] {featur,
## model,
## order} => {show} 0.1000000 1.0000000 1.875000 3
## [6778] {algorithm,
## power,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [6779] {train,
## power,
## specif} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6780] {train,
## algorithm,
## power} => {specif} 0.1000000 1.0000000 6.000000 3
## [6781] {train,
## algorithm,
## specif} => {power} 0.1000000 0.7500000 5.625000 3
## [6782] {dataset,
## demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6783] {demonstr,
## learn,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6784] {dataset,
## learn,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [6785] {dataset,
## demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6786] {dataset,
## demonstr,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [6787] {model,
## demonstr,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6788] {model,
## dataset,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [6789] {model,
## dataset,
## demonstr} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6790] {dataset,
## demonstr,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6791] {featur,
## demonstr,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6792] {featur,
## dataset,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [6793] {featur,
## dataset,
## demonstr} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6794] {demonstr,
## learn,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [6795] {model,
## demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6796] {model,
## learn,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [6797] {model,
## demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6798] {demonstr,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6799] {featur,
## demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6800] {featur,
## learn,
## benchmark} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [6801] {featur,
## demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6802] {model,
## demonstr,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6803] {featur,
## demonstr,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [6804] {featur,
## model,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [6805] {featur,
## model,
## demonstr} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6806] {classif,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6807] {classif,
## featur,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6808] {featur,
## learn,
## benchmark} => {classif} 0.1000000 0.7500000 2.812500 3
## [6809] {perform,
## problem,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6810] {problem,
## learn,
## benchmark} => {perform} 0.1000000 1.0000000 2.142857 3
## [6811] {perform,
## learn,
## benchmark} => {problem} 0.1000000 1.0000000 3.333333 3
## [6812] {perform,
## problem,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6813] {featur,
## problem,
## benchmark} => {perform} 0.1000000 1.0000000 2.142857 3
## [6814] {featur,
## perform,
## benchmark} => {problem} 0.1000000 1.0000000 3.333333 3
## [6815] {problem,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6816] {featur,
## problem,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6817] {featur,
## learn,
## benchmark} => {problem} 0.1000000 0.7500000 2.500000 3
## [6818] {featur,
## problem,
## learn} => {benchmark} 0.1000000 0.7500000 5.625000 3
## [6819] {perform,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6820] {featur,
## perform,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6821] {featur,
## learn,
## benchmark} => {perform} 0.1000000 0.7500000 1.607143 3
## [6822] {dataset,
## learn,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [6823] {model,
## dataset,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6824] {model,
## learn,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6825] {dataset,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6826] {featur,
## dataset,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6827] {featur,
## learn,
## benchmark} => {dataset} 0.1000000 0.7500000 1.730769 3
## [6828] {model,
## dataset,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6829] {featur,
## dataset,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [6830] {featur,
## model,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6831] {propos,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6832] {featur,
## learn,
## benchmark} => {propos} 0.1000000 0.7500000 1.500000 3
## [6833] {featur,
## propos,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6834] {model,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6835] {featur,
## learn,
## benchmark} => {model} 0.1000000 0.7500000 1.406250 3
## [6836] {featur,
## model,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6837] {represent,
## success,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6838] {propos,
## success,
## high} => {represent} 0.1000000 1.0000000 2.000000 3
## [6839] {represent,
## propos,
## high} => {success} 0.1000000 1.0000000 3.750000 3
## [6840] {represent,
## success,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6841] {featur,
## success,
## high} => {represent} 0.1000000 1.0000000 2.000000 3
## [6842] {featur,
## represent,
## high} => {success} 0.1000000 1.0000000 3.750000 3
## [6843] {propos,
## success,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6844] {featur,
## success,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6845] {featur,
## propos,
## high} => {success} 0.1000000 0.7500000 2.812500 3
## [6846] {featur,
## propos,
## success} => {high} 0.1000000 0.7500000 5.625000 3
## [6847] {object,
## propos,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6848] {featur,
## object,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6849] {featur,
## propos,
## high} => {object} 0.1000000 0.7500000 2.812500 3
## [6850] {classif,
## learn,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6851] {classif,
## propos,
## high} => {learn} 0.1000000 1.0000000 2.307692 3
## [6852] {propos,
## learn,
## high} => {classif} 0.1000000 1.0000000 3.750000 3
## [6853] {classif,
## propos,
## learn} => {high} 0.1000000 0.7500000 5.625000 3
## [6854] {classif,
## learn,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6855] {classif,
## featur,
## high} => {learn} 0.1000000 1.0000000 2.307692 3
## [6856] {featur,
## learn,
## high} => {classif} 0.1000000 1.0000000 3.750000 3
## [6857] {classif,
## propos,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6858] {classif,
## featur,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6859] {featur,
## propos,
## high} => {classif} 0.1000000 0.7500000 2.812500 3
## [6860] {task,
## propos,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6861] {featur,
## task,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6862] {featur,
## propos,
## high} => {task} 0.1000000 0.7500000 2.045455 3
## [6863] {propos,
## learn,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6864] {featur,
## learn,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6865] {featur,
## propos,
## high} => {learn} 0.1000000 0.7500000 1.730769 3
## [6866] {represent,
## propos,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6867] {featur,
## represent,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6868] {featur,
## propos,
## high} => {represent} 0.1000000 0.7500000 1.500000 3
## [6869] {reduc,
## comput,
## parallel} => {network} 0.1000000 1.0000000 1.578947 3
## [6870] {network,
## comput,
## parallel} => {reduc} 0.1000000 1.0000000 4.285714 3
## [6871] {network,
## reduc,
## parallel} => {comput} 0.1000000 1.0000000 4.285714 3
## [6872] {network,
## reduc,
## comput} => {parallel} 0.1000000 1.0000000 7.500000 3
## [6873] {outperform,
## predict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6874] {outperform,
## predict,
## show} => {train} 0.1000000 1.0000000 2.500000 3
## [6875] {predict,
## show,
## train} => {outperform} 0.1000000 1.0000000 7.500000 3
## [6876] {outperform,
## show,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [6877] {predict,
## train,
## challeng} => {perform} 0.1000000 1.0000000 2.142857 3
## [6878] {predict,
## perform,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [6879] {predict,
## train,
## perform} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6880] {train,
## perform,
## challeng} => {predict} 0.1000000 1.0000000 7.500000 3
## [6881] {predict,
## train,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [6882] {predict,
## propos,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [6883] {predict,
## train,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6884] {train,
## propos,
## challeng} => {predict} 0.1000000 1.0000000 7.500000 3
## [6885] {predict,
## perform,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [6886] {predict,
## propos,
## challeng} => {perform} 0.1000000 1.0000000 2.142857 3
## [6887] {predict,
## perform,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6888] {perform,
## propos,
## challeng} => {predict} 0.1000000 1.0000000 7.500000 3
## [6889] {paper,
## predict,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [6890] {data,
## paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6891] {data,
## predict,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [6892] {data,
## paper,
## train} => {predict} 0.1000000 0.7500000 5.625000 3
## [6893] {paper,
## predict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6894] {model,
## paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6895] {model,
## predict,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [6896] {model,
## paper,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [6897] {paper,
## predict,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [6898] {featur,
## paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6899] {featur,
## predict,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [6900] {data,
## paper,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [6901] {model,
## paper,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [6902] {data,
## model,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [6903] {data,
## model,
## paper} => {predict} 0.1000000 0.7500000 5.625000 3
## [6904] {data,
## paper,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6905] {featur,
## paper,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [6906] {data,
## featur,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [6907] {data,
## featur,
## paper} => {predict} 0.1000000 0.7500000 5.625000 3
## [6908] {model,
## paper,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6909] {featur,
## paper,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [6910] {featur,
## model,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [6911] {featur,
## model,
## paper} => {predict} 0.1000000 1.0000000 7.500000 3
## [6912] {predict,
## train,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [6913] {predict,
## train,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [6914] {predict,
## perform,
## propos} => {train} 0.1000000 1.0000000 2.500000 3
## [6915] {train,
## perform,
## propos} => {predict} 0.1000000 0.7500000 5.625000 3
## [6916] {data,
## predict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6917] {model,
## predict,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [6918] {data,
## model,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6919] {data,
## model,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [6920] {data,
## predict,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [6921] {featur,
## predict,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [6922] {data,
## featur,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6923] {model,
## predict,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [6924] {featur,
## predict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6925] {featur,
## model,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6926] {featur,
## model,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [6927] {data,
## model,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6928] {data,
## featur,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [6929] {featur,
## model,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [6930] {data,
## make,
## number} => {model} 0.1000000 1.0000000 1.875000 3
## [6931] {make,
## model,
## number} => {data} 0.1000000 1.0000000 2.307692 3
## [6932] {data,
## model,
## number} => {make} 0.1000000 1.0000000 3.333333 3
## [6933] {data,
## make,
## model} => {number} 0.1000000 0.7500000 4.500000 3
## [6934] {recognit,
## challeng,
## face} => {train} 0.1000000 1.0000000 2.500000 3
## [6935] {train,
## challeng,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6936] {train,
## recognit,
## face} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6937] {train,
## recognit,
## challeng} => {face} 0.1000000 1.0000000 7.500000 3
## [6938] {recognit,
## challeng,
## face} => {represent} 0.1000000 1.0000000 2.000000 3
## [6939] {represent,
## challeng,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6940] {represent,
## recognit,
## face} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6941] {represent,
## recognit,
## challeng} => {face} 0.1000000 1.0000000 7.500000 3
## [6942] {train,
## challeng,
## face} => {represent} 0.1000000 1.0000000 2.000000 3
## [6943] {represent,
## challeng,
## face} => {train} 0.1000000 1.0000000 2.500000 3
## [6944] {represent,
## train,
## face} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6945] {represent,
## train,
## challeng} => {face} 0.1000000 1.0000000 7.500000 3
## [6946] {recognit,
## imag,
## face} => {propos} 0.1000000 1.0000000 2.000000 3
## [6947] {propos,
## imag,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6948] {propos,
## recognit,
## face} => {imag} 0.1000000 1.0000000 6.000000 3
## [6949] {propos,
## recognit,
## imag} => {face} 0.1000000 0.7500000 5.625000 3
## [6950] {train,
## recognit,
## face} => {represent} 0.1000000 1.0000000 2.000000 3
## [6951] {represent,
## recognit,
## face} => {train} 0.1000000 1.0000000 2.500000 3
## [6952] {represent,
## train,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6953] {represent,
## train,
## recognit} => {face} 0.1000000 0.7500000 5.625000 3
## [6954] {data,
## recognit,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6955] {dataset,
## recognit,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [6956] {data,
## dataset,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6957] {data,
## dataset,
## recognit} => {face} 0.1000000 0.7500000 5.625000 3
## [6958] {data,
## recognit,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [6959] {recognit,
## learn,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [6960] {data,
## learn,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6961] {data,
## recognit,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [6962] {featur,
## recognit,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [6963] {data,
## featur,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6964] {data,
## featur,
## recognit} => {face} 0.1000000 0.7500000 5.625000 3
## [6965] {dataset,
## recognit,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [6966] {recognit,
## learn,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6967] {dataset,
## learn,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6968] {dataset,
## recognit,
## learn} => {face} 0.1000000 0.7500000 5.625000 3
## [6969] {dataset,
## recognit,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [6970] {featur,
## recognit,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6971] {featur,
## dataset,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6972] {featur,
## dataset,
## recognit} => {face} 0.1000000 0.7500000 5.625000 3
## [6973] {recognit,
## learn,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [6974] {featur,
## recognit,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [6975] {featur,
## learn,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6976] {data,
## dataset,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [6977] {data,
## learn,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6978] {dataset,
## learn,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [6979] {data,
## dataset,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [6980] {data,
## featur,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6981] {featur,
## dataset,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [6982] {data,
## learn,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [6983] {data,
## featur,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [6984] {featur,
## learn,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [6985] {dataset,
## learn,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [6986] {featur,
## dataset,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [6987] {featur,
## learn,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6988] {approach,
## method,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6989] {method,
## dataset,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [6990] {approach,
## dataset,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [6991] {approach,
## method,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [6992] {method,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [6993] {approach,
## learn,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [6994] {approach,
## method,
## learn} => {sourc} 0.1000000 0.7500000 5.625000 3
## [6995] {approach,
## method,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [6996] {method,
## show,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [6997] {approach,
## show,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [6998] {approach,
## method,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [6999] {method,
## model,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [7000] {approach,
## model,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [7001] {method,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7002] {method,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7003] {dataset,
## learn,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [7004] {method,
## dataset,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [7005] {method,
## dataset,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7006] {method,
## show,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7007] {show,
## dataset,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [7008] {method,
## dataset,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [7009] {method,
## model,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7010] {model,
## dataset,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [7011] {method,
## model,
## dataset} => {sourc} 0.1000000 0.7500000 5.625000 3
## [7012] {method,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7013] {method,
## show,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7014] {show,
## learn,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [7015] {method,
## show,
## learn} => {sourc} 0.1000000 0.7500000 5.625000 3
## [7016] {method,
## learn,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [7017] {method,
## model,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7018] {model,
## learn,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [7019] {method,
## model,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [7020] {method,
## show,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [7021] {method,
## model,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7022] {model,
## show,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [7023] {approach,
## dataset,
## sourc} => {learn} 0.1333333 1.0000000 2.307692 4
## [7024] {approach,
## learn,
## sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [7025] {dataset,
## learn,
## sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [7026] {approach,
## dataset,
## learn} => {sourc} 0.1333333 0.8000000 6.000000 4
## [7027] {approach,
## dataset,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [7028] {approach,
## represent,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7029] {represent,
## dataset,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [7030] {approach,
## represent,
## dataset} => {sourc} 0.1000000 0.7500000 5.625000 3
## [7031] {approach,
## dataset,
## sourc} => {show} 0.1333333 1.0000000 1.875000 4
## [7032] {approach,
## show,
## sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [7033] {show,
## dataset,
## sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [7034] {approach,
## dataset,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [7035] {approach,
## propos,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7036] {dataset,
## propos,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [7037] {approach,
## dataset,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [7038] {approach,
## model,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7039] {model,
## dataset,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [7040] {approach,
## learn,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [7041] {approach,
## represent,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7042] {represent,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [7043] {approach,
## learn,
## sourc} => {show} 0.1333333 1.0000000 1.875000 4
## [7044] {approach,
## show,
## sourc} => {learn} 0.1333333 1.0000000 2.307692 4
## [7045] {show,
## learn,
## sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [7046] {approach,
## show,
## learn} => {sourc} 0.1333333 0.8000000 6.000000 4
## [7047] {approach,
## learn,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [7048] {approach,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7049] {propos,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [7050] {approach,
## learn,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [7051] {approach,
## model,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7052] {model,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [7053] {approach,
## represent,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7054] {approach,
## show,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [7055] {represent,
## show,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [7056] {approach,
## represent,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [7057] {approach,
## propos,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [7058] {represent,
## propos,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [7059] {approach,
## show,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [7060] {approach,
## propos,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7061] {show,
## propos,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [7062] {approach,
## show,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [7063] {approach,
## model,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7064] {model,
## show,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [7065] {dataset,
## learn,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [7066] {represent,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7067] {represent,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7068] {dataset,
## learn,
## sourc} => {show} 0.1333333 1.0000000 1.875000 4
## [7069] {show,
## dataset,
## sourc} => {learn} 0.1333333 1.0000000 2.307692 4
## [7070] {show,
## learn,
## sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [7071] {show,
## dataset,
## learn} => {sourc} 0.1333333 0.8000000 6.000000 4
## [7072] {dataset,
## learn,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [7073] {dataset,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7074] {propos,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7075] {dataset,
## learn,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [7076] {model,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7077] {model,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7078] {represent,
## dataset,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7079] {show,
## dataset,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [7080] {represent,
## show,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7081] {represent,
## show,
## dataset} => {sourc} 0.1000000 0.7500000 5.625000 3
## [7082] {represent,
## dataset,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [7083] {dataset,
## propos,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [7084] {represent,
## propos,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7085] {show,
## dataset,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [7086] {dataset,
## propos,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7087] {show,
## propos,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7088] {show,
## dataset,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [7089] {model,
## dataset,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7090] {model,
## show,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7091] {model,
## show,
## dataset} => {sourc} 0.1000000 0.7500000 5.625000 3
## [7092] {represent,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7093] {show,
## learn,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [7094] {represent,
## show,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7095] {represent,
## learn,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [7096] {propos,
## learn,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [7097] {represent,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7098] {show,
## learn,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [7099] {propos,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7100] {show,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7101] {show,
## learn,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [7102] {model,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7103] {model,
## show,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7104] {represent,
## show,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [7105] {represent,
## propos,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [7106] {show,
## propos,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [7107] {general,
## recognit,
## variat} => {represent} 0.1000000 1.0000000 2.000000 3
## [7108] {represent,
## general,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [7109] {represent,
## recognit,
## variat} => {general} 0.1000000 1.0000000 5.000000 3
## [7110] {represent,
## general,
## recognit} => {variat} 0.1000000 1.0000000 7.500000 3
## [7111] {general,
## recognit,
## variat} => {show} 0.1000000 1.0000000 1.875000 3
## [7112] {show,
## general,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [7113] {show,
## recognit,
## variat} => {general} 0.1000000 1.0000000 5.000000 3
## [7114] {show,
## general,
## recognit} => {variat} 0.1000000 0.7500000 5.625000 3
## [7115] {represent,
## general,
## variat} => {show} 0.1000000 1.0000000 1.875000 3
## [7116] {show,
## general,
## variat} => {represent} 0.1000000 1.0000000 2.000000 3
## [7117] {represent,
## show,
## variat} => {general} 0.1000000 1.0000000 5.000000 3
## [7118] {represent,
## show,
## general} => {variat} 0.1000000 0.7500000 5.625000 3
## [7119] {task,
## recognit,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [7120] {data,
## recognit,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [7121] {data,
## task,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [7122] {data,
## task,
## recognit} => {variat} 0.1000000 0.7500000 5.625000 3
## [7123] {task,
## recognit,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [7124] {recognit,
## learn,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [7125] {task,
## learn,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [7126] {task,
## recognit,
## learn} => {variat} 0.1000000 0.7500000 5.625000 3
## [7127] {task,
## recognit,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [7128] {featur,
## recognit,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [7129] {featur,
## task,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [7130] {featur,
## task,
## recognit} => {variat} 0.1000000 1.0000000 7.500000 3
## [7131] {data,
## recognit,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [7132] {recognit,
## learn,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [7133] {data,
## learn,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [7134] {data,
## recognit,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [7135] {featur,
## recognit,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [7136] {data,
## featur,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [7137] {data,
## featur,
## recognit} => {variat} 0.1000000 0.7500000 5.625000 3
## [7138] {recognit,
## learn,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [7139] {featur,
## recognit,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [7140] {featur,
## learn,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [7141] {represent,
## recognit,
## variat} => {show} 0.1000000 1.0000000 1.875000 3
## [7142] {show,
## recognit,
## variat} => {represent} 0.1000000 1.0000000 2.000000 3
## [7143] {represent,
## show,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [7144] {data,
## task,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [7145] {task,
## learn,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [7146] {data,
## learn,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [7147] {data,
## task,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [7148] {featur,
## task,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [7149] {data,
## featur,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [7150] {task,
## learn,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [7151] {featur,
## task,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [7152] {featur,
## learn,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [7153] {data,
## learn,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [7154] {data,
## featur,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [7155] {featur,
## learn,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [7156] {process,
## introduc,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [7157] {model,
## process,
## introduc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7158] {model,
## introduc,
## learn} => {process} 0.1000000 1.0000000 5.000000 3
## [7159] {model,
## process,
## learn} => {introduc} 0.1000000 1.0000000 7.500000 3
## [7160] {process,
## introduc,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [7161] {featur,
## process,
## introduc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7162] {featur,
## introduc,
## learn} => {process} 0.1000000 1.0000000 5.000000 3
## [7163] {featur,
## process,
## learn} => {introduc} 0.1000000 1.0000000 7.500000 3
## [7164] {model,
## process,
## introduc} => {featur} 0.1000000 1.0000000 1.875000 3
## [7165] {featur,
## process,
## introduc} => {model} 0.1000000 1.0000000 1.875000 3
## [7166] {featur,
## model,
## introduc} => {process} 0.1000000 1.0000000 5.000000 3
## [7167] {featur,
## model,
## process} => {introduc} 0.1000000 0.7500000 5.625000 3
## [7168] {model,
## introduc,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [7169] {featur,
## introduc,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [7170] {featur,
## model,
## introduc} => {learn} 0.1000000 1.0000000 2.307692 3
## [7171] {approach,
## import,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [7172] {data,
## import,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [7173] {approach,
## data,
## import} => {task} 0.1000000 1.0000000 2.727273 3
## [7174] {approach,
## import,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [7175] {import,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [7176] {approach,
## import,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [7177] {approach,
## import,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [7178] {import,
## model,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [7179] {approach,
## import,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [7180] {approach,
## model,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [7181] {approach,
## import,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7182] {featur,
## import,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [7183] {approach,
## featur,
## import} => {task} 0.1000000 1.0000000 2.727273 3
## [7184] {data,
## import,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [7185] {import,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [7186] {data,
## import,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [7187] {data,
## import,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [7188] {import,
## represent,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [7189] {data,
## import,
## represent} => {task} 0.1333333 1.0000000 2.727273 4
## [7190] {data,
## import,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [7191] {import,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [7192] {data,
## import,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [7193] {data,
## import,
## task} => {model} 0.1333333 1.0000000 1.875000 4
## [7194] {import,
## model,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [7195] {data,
## import,
## model} => {task} 0.1333333 1.0000000 2.727273 4
## [7196] {data,
## import,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [7197] {featur,
## import,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [7198] {data,
## featur,
## import} => {task} 0.1333333 1.0000000 2.727273 4
## [7199] {import,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [7200] {import,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [7201] {import,
## represent,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [7202] {import,
## task,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [7203] {import,
## model,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [7204] {import,
## model,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [7205] {import,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [7206] {featur,
## import,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [7207] {featur,
## import,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [7208] {import,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [7209] {import,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [7210] {import,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [7211] {import,
## represent,
## task} => {model} 0.1333333 1.0000000 1.875000 4
## [7212] {import,
## model,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [7213] {import,
## model,
## represent} => {task} 0.1333333 1.0000000 2.727273 4
## [7214] {model,
## represent,
## task} => {import} 0.1333333 1.0000000 7.500000 4
## [7215] {import,
## represent,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [7216] {featur,
## import,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [7217] {featur,
## import,
## represent} => {task} 0.1333333 1.0000000 2.727273 4
## [7218] {import,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [7219] {import,
## model,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [7220] {import,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [7221] {model,
## show,
## task} => {import} 0.1000000 0.7500000 5.625000 3
## [7222] {import,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7223] {featur,
## import,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [7224] {featur,
## import,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [7225] {import,
## model,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [7226] {featur,
## import,
## task} => {model} 0.1333333 1.0000000 1.875000 4
## [7227] {featur,
## import,
## model} => {task} 0.1333333 1.0000000 2.727273 4
## [7228] {approach,
## data,
## import} => {represent} 0.1000000 1.0000000 2.000000 3
## [7229] {approach,
## import,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [7230] {data,
## import,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [7231] {approach,
## data,
## import} => {model} 0.1000000 1.0000000 1.875000 3
## [7232] {approach,
## import,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [7233] {data,
## import,
## model} => {approach} 0.1000000 0.7500000 1.875000 3
## [7234] {approach,
## data,
## model} => {import} 0.1000000 1.0000000 7.500000 3
## [7235] {approach,
## data,
## import} => {featur} 0.1000000 1.0000000 1.875000 3
## [7236] {approach,
## featur,
## import} => {data} 0.1000000 1.0000000 2.307692 3
## [7237] {data,
## featur,
## import} => {approach} 0.1000000 0.7500000 1.875000 3
## [7238] {approach,
## data,
## featur} => {import} 0.1000000 0.7500000 5.625000 3
## [7239] {approach,
## import,
## represent} => {model} 0.1000000 1.0000000 1.875000 3
## [7240] {approach,
## import,
## model} => {represent} 0.1000000 1.0000000 2.000000 3
## [7241] {import,
## model,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [7242] {approach,
## import,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [7243] {approach,
## featur,
## import} => {represent} 0.1000000 1.0000000 2.000000 3
## [7244] {featur,
## import,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [7245] {approach,
## import,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [7246] {approach,
## featur,
## import} => {model} 0.1000000 1.0000000 1.875000 3
## [7247] {featur,
## import,
## model} => {approach} 0.1000000 0.7500000 1.875000 3
## [7248] {data,
## import,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [7249] {data,
## import,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [7250] {import,
## represent,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [7251] {data,
## import,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [7252] {data,
## import,
## model} => {learn} 0.1000000 0.7500000 1.730769 3
## [7253] {import,
## model,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [7254] {data,
## import,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [7255] {data,
## featur,
## import} => {learn} 0.1000000 0.7500000 1.730769 3
## [7256] {featur,
## import,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [7257] {data,
## import,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [7258] {data,
## import,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [7259] {import,
## represent,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [7260] {data,
## import,
## represent} => {model} 0.1333333 1.0000000 1.875000 4
## [7261] {data,
## import,
## model} => {represent} 0.1333333 1.0000000 2.000000 4
## [7262] {import,
## model,
## represent} => {data} 0.1333333 1.0000000 2.307692 4
## [7263] {data,
## model,
## represent} => {import} 0.1333333 0.8000000 6.000000 4
## [7264] {data,
## import,
## represent} => {featur} 0.1333333 1.0000000 1.875000 4
## [7265] {data,
## featur,
## import} => {represent} 0.1333333 1.0000000 2.000000 4
## [7266] {featur,
## import,
## represent} => {data} 0.1333333 1.0000000 2.307692 4
## [7267] {data,
## import,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7268] {data,
## import,
## model} => {show} 0.1000000 0.7500000 1.406250 3
## [7269] {import,
## model,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [7270] {data,
## import,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [7271] {data,
## featur,
## import} => {show} 0.1000000 0.7500000 1.406250 3
## [7272] {featur,
## import,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [7273] {data,
## import,
## model} => {featur} 0.1333333 1.0000000 1.875000 4
## [7274] {data,
## featur,
## import} => {model} 0.1333333 1.0000000 1.875000 4
## [7275] {featur,
## import,
## model} => {data} 0.1333333 1.0000000 2.307692 4
## [7276] {import,
## represent,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [7277] {import,
## model,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [7278] {import,
## model,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [7279] {import,
## represent,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [7280] {featur,
## import,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [7281] {featur,
## import,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [7282] {import,
## model,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [7283] {featur,
## import,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [7284] {featur,
## import,
## model} => {learn} 0.1000000 0.7500000 1.730769 3
## [7285] {import,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7286] {import,
## model,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [7287] {import,
## model,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [7288] {import,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [7289] {featur,
## import,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [7290] {featur,
## import,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [7291] {import,
## model,
## represent} => {featur} 0.1333333 1.0000000 1.875000 4
## [7292] {featur,
## import,
## represent} => {model} 0.1333333 1.0000000 1.875000 4
## [7293] {featur,
## import,
## model} => {represent} 0.1333333 1.0000000 2.000000 4
## [7294] {import,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [7295] {featur,
## import,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7296] {featur,
## import,
## model} => {show} 0.1000000 0.7500000 1.406250 3
## [7297] {problem,
## art,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [7298] {perform,
## art,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [7299] {perform,
## problem,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7300] {perform,
## problem,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7301] {problem,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7302] {learn,
## art,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [7303] {problem,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7304] {problem,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7305] {problem,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7306] {propos,
## art,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [7307] {propos,
## problem,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7308] {propos,
## problem,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7309] {problem,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7310] {model,
## art,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [7311] {model,
## problem,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7312] {model,
## problem,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7313] {task,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [7314] {data,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [7315] {data,
## task,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7316] {data,
## task,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7317] {task,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7318] {learn,
## art,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [7319] {task,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7320] {task,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7321] {task,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7322] {propos,
## art,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [7323] {task,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7324] {task,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7325] {task,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7326] {model,
## art,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [7327] {model,
## task,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7328] {model,
## task,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7329] {task,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [7330] {featur,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [7331] {featur,
## task,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7332] {featur,
## task,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7333] {perform,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7334] {learn,
## art,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [7335] {perform,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7336] {perform,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7337] {perform,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7338] {propos,
## art,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [7339] {perform,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7340] {perform,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7341] {perform,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7342] {model,
## art,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [7343] {model,
## perform,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7344] {model,
## perform,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7345] {data,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7346] {learn,
## art,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [7347] {data,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7348] {data,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7349] {data,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7350] {propos,
## art,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [7351] {data,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7352] {data,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7353] {data,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7354] {model,
## art,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [7355] {data,
## model,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7356] {data,
## model,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7357] {data,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [7358] {featur,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [7359] {data,
## featur,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7360] {data,
## featur,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7361] {dataset,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7362] {learn,
## art,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [7363] {dataset,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7364] {dataset,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7365] {dataset,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7366] {propos,
## art,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [7367] {dataset,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7368] {dataset,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7369] {dataset,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7370] {model,
## art,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [7371] {model,
## dataset,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7372] {model,
## dataset,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7373] {learn,
## art,
## state} => {propos} 0.1333333 1.0000000 2.000000 4
## [7374] {propos,
## art,
## state} => {learn} 0.1333333 1.0000000 2.307692 4
## [7375] {propos,
## learn,
## art} => {state} 0.1333333 1.0000000 7.500000 4
## [7376] {propos,
## learn,
## state} => {art} 0.1333333 1.0000000 7.500000 4
## [7377] {learn,
## art,
## state} => {model} 0.1333333 1.0000000 1.875000 4
## [7378] {model,
## art,
## state} => {learn} 0.1333333 1.0000000 2.307692 4
## [7379] {model,
## learn,
## art} => {state} 0.1333333 1.0000000 7.500000 4
## [7380] {model,
## learn,
## state} => {art} 0.1333333 1.0000000 7.500000 4
## [7381] {learn,
## art,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [7382] {featur,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7383] {featur,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7384] {featur,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7385] {propos,
## art,
## state} => {model} 0.1333333 1.0000000 1.875000 4
## [7386] {model,
## art,
## state} => {propos} 0.1333333 1.0000000 2.000000 4
## [7387] {model,
## propos,
## art} => {state} 0.1333333 1.0000000 7.500000 4
## [7388] {model,
## propos,
## state} => {art} 0.1333333 1.0000000 7.500000 4
## [7389] {propos,
## art,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [7390] {featur,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7391] {featur,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7392] {featur,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7393] {model,
## art,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [7394] {featur,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7395] {featur,
## model,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [7396] {featur,
## model,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [7397] {perform,
## problem,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7398] {problem,
## learn,
## art} => {perform} 0.1000000 1.0000000 2.142857 3
## [7399] {perform,
## learn,
## art} => {problem} 0.1000000 1.0000000 3.333333 3
## [7400] {perform,
## problem,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7401] {propos,
## problem,
## art} => {perform} 0.1000000 1.0000000 2.142857 3
## [7402] {perform,
## propos,
## art} => {problem} 0.1000000 1.0000000 3.333333 3
## [7403] {perform,
## problem,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7404] {model,
## problem,
## art} => {perform} 0.1000000 1.0000000 2.142857 3
## [7405] {model,
## perform,
## art} => {problem} 0.1000000 1.0000000 3.333333 3
## [7406] {problem,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7407] {propos,
## problem,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7408] {propos,
## learn,
## art} => {problem} 0.1000000 0.7500000 2.500000 3
## [7409] {propos,
## problem,
## learn} => {art} 0.1000000 0.7500000 5.625000 3
## [7410] {problem,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7411] {model,
## problem,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7412] {model,
## learn,
## art} => {problem} 0.1000000 0.7500000 2.500000 3
## [7413] {model,
## problem,
## learn} => {art} 0.1000000 0.7500000 5.625000 3
## [7414] {propos,
## problem,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7415] {model,
## problem,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7416] {model,
## propos,
## art} => {problem} 0.1000000 0.7500000 2.500000 3
## [7417] {model,
## propos,
## problem} => {art} 0.1000000 1.0000000 7.500000 3
## [7418] {data,
## task,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7419] {task,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [7420] {data,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [7421] {data,
## task,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7422] {task,
## propos,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [7423] {data,
## propos,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [7424] {data,
## task,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7425] {model,
## task,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [7426] {data,
## model,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [7427] {data,
## task,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [7428] {featur,
## task,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [7429] {data,
## featur,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [7430] {task,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7431] {task,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7432] {propos,
## learn,
## art} => {task} 0.1000000 0.7500000 2.045455 3
## [7433] {task,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7434] {model,
## task,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7435] {model,
## learn,
## art} => {task} 0.1000000 0.7500000 2.045455 3
## [7436] {task,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [7437] {featur,
## task,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7438] {featur,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [7439] {task,
## propos,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7440] {model,
## task,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7441] {model,
## propos,
## art} => {task} 0.1000000 0.7500000 2.045455 3
## [7442] {model,
## task,
## propos} => {art} 0.1000000 0.7500000 5.625000 3
## [7443] {task,
## propos,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [7444] {featur,
## task,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7445] {featur,
## propos,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [7446] {model,
## task,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [7447] {featur,
## task,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7448] {featur,
## model,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [7449] {perform,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7450] {perform,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7451] {propos,
## learn,
## art} => {perform} 0.1000000 0.7500000 1.607143 3
## [7452] {perform,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7453] {model,
## perform,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7454] {model,
## learn,
## art} => {perform} 0.1000000 0.7500000 1.607143 3
## [7455] {perform,
## propos,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7456] {model,
## perform,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7457] {model,
## propos,
## art} => {perform} 0.1000000 0.7500000 1.607143 3
## [7458] {data,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7459] {data,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7460] {propos,
## learn,
## art} => {data} 0.1000000 0.7500000 1.730769 3
## [7461] {data,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7462] {data,
## model,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7463] {model,
## learn,
## art} => {data} 0.1000000 0.7500000 1.730769 3
## [7464] {data,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [7465] {data,
## featur,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7466] {featur,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [7467] {data,
## propos,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7468] {data,
## model,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7469] {model,
## propos,
## art} => {data} 0.1000000 0.7500000 1.730769 3
## [7470] {data,
## propos,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [7471] {data,
## featur,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7472] {featur,
## propos,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [7473] {data,
## model,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [7474] {data,
## featur,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7475] {featur,
## model,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [7476] {dataset,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7477] {dataset,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7478] {propos,
## learn,
## art} => {dataset} 0.1000000 0.7500000 1.730769 3
## [7479] {dataset,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7480] {model,
## dataset,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7481] {model,
## learn,
## art} => {dataset} 0.1000000 0.7500000 1.730769 3
## [7482] {dataset,
## propos,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7483] {model,
## dataset,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7484] {model,
## propos,
## art} => {dataset} 0.1000000 0.7500000 1.730769 3
## [7485] {propos,
## learn,
## art} => {model} 0.1333333 1.0000000 1.875000 4
## [7486] {model,
## learn,
## art} => {propos} 0.1333333 1.0000000 2.000000 4
## [7487] {model,
## propos,
## art} => {learn} 0.1333333 1.0000000 2.307692 4
## [7488] {propos,
## learn,
## art} => {featur} 0.1000000 0.7500000 1.406250 3
## [7489] {featur,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7490] {featur,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7491] {model,
## learn,
## art} => {featur} 0.1000000 0.7500000 1.406250 3
## [7492] {featur,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7493] {featur,
## model,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [7494] {model,
## propos,
## art} => {featur} 0.1000000 0.7500000 1.406250 3
## [7495] {featur,
## propos,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [7496] {featur,
## model,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [7497] {perform,
## problem,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7498] {problem,
## learn,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [7499] {perform,
## learn,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [7500] {perform,
## problem,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7501] {propos,
## problem,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [7502] {perform,
## propos,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [7503] {perform,
## problem,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7504] {model,
## problem,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [7505] {model,
## perform,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [7506] {problem,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7507] {propos,
## problem,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7508] {propos,
## learn,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [7509] {propos,
## problem,
## learn} => {state} 0.1000000 0.7500000 5.625000 3
## [7510] {problem,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7511] {model,
## problem,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7512] {model,
## learn,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [7513] {model,
## problem,
## learn} => {state} 0.1000000 0.7500000 5.625000 3
## [7514] {propos,
## problem,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7515] {model,
## problem,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7516] {model,
## propos,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [7517] {model,
## propos,
## problem} => {state} 0.1000000 1.0000000 7.500000 3
## [7518] {data,
## task,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7519] {task,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [7520] {data,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [7521] {data,
## task,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7522] {task,
## propos,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [7523] {data,
## propos,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [7524] {data,
## task,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7525] {model,
## task,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [7526] {data,
## model,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [7527] {data,
## task,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [7528] {featur,
## task,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [7529] {data,
## featur,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [7530] {task,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7531] {task,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7532] {propos,
## learn,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [7533] {task,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7534] {model,
## task,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7535] {model,
## learn,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [7536] {task,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [7537] {featur,
## task,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7538] {featur,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [7539] {task,
## propos,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7540] {model,
## task,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7541] {model,
## propos,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [7542] {model,
## task,
## propos} => {state} 0.1000000 0.7500000 5.625000 3
## [7543] {task,
## propos,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [7544] {featur,
## task,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7545] {featur,
## propos,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [7546] {model,
## task,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [7547] {featur,
## task,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7548] {featur,
## model,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [7549] {perform,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7550] {perform,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7551] {propos,
## learn,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [7552] {perform,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7553] {model,
## perform,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7554] {model,
## learn,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [7555] {perform,
## propos,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7556] {model,
## perform,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7557] {model,
## propos,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [7558] {data,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7559] {data,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7560] {propos,
## learn,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [7561] {data,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7562] {data,
## model,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7563] {model,
## learn,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [7564] {data,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [7565] {data,
## featur,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7566] {featur,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [7567] {data,
## propos,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7568] {data,
## model,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7569] {model,
## propos,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [7570] {data,
## propos,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [7571] {data,
## featur,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7572] {featur,
## propos,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [7573] {data,
## model,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [7574] {data,
## featur,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7575] {featur,
## model,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [7576] {dataset,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7577] {dataset,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7578] {propos,
## learn,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [7579] {dataset,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7580] {model,
## dataset,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7581] {model,
## learn,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [7582] {dataset,
## propos,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7583] {model,
## dataset,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7584] {model,
## propos,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [7585] {propos,
## learn,
## state} => {model} 0.1333333 1.0000000 1.875000 4
## [7586] {model,
## learn,
## state} => {propos} 0.1333333 1.0000000 2.000000 4
## [7587] {model,
## propos,
## state} => {learn} 0.1333333 1.0000000 2.307692 4
## [7588] {propos,
## learn,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [7589] {featur,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7590] {featur,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7591] {model,
## learn,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [7592] {featur,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7593] {featur,
## model,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [7594] {model,
## propos,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [7595] {featur,
## propos,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [7596] {featur,
## model,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [7597] {general,
## joint,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [7598] {show,
## general,
## joint} => {perform} 0.1000000 1.0000000 2.142857 3
## [7599] {show,
## joint,
## perform} => {general} 0.1000000 1.0000000 5.000000 3
## [7600] {show,
## general,
## perform} => {joint} 0.1000000 1.0000000 7.500000 3
## [7601] {general,
## joint,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [7602] {general,
## joint,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [7603] {joint,
## perform,
## propos} => {general} 0.1000000 1.0000000 5.000000 3
## [7604] {general,
## perform,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [7605] {show,
## general,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7606] {general,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7607] {show,
## joint,
## propos} => {general} 0.1000000 0.7500000 3.750000 3
## [7608] {show,
## general,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [7609] {show,
## joint,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [7610] {joint,
## object,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7611] {show,
## joint,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [7612] {approach,
## method,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7613] {method,
## dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7614] {approach,
## dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7615] {approach,
## method,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7616] {method,
## show,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7617] {approach,
## show,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7618] {approach,
## method,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7619] {method,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [7620] {approach,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [7621] {approach,
## method,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7622] {method,
## model,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7623] {approach,
## model,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7624] {method,
## dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7625] {method,
## show,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7626] {show,
## dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7627] {method,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7628] {method,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7629] {dataset,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [7630] {method,
## dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7631] {method,
## model,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7632] {model,
## dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7633] {method,
## model,
## dataset} => {joint} 0.1000000 0.7500000 5.625000 3
## [7634] {method,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7635] {method,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7636] {show,
## joint,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [7637] {method,
## show,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7638] {method,
## model,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7639] {model,
## show,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7640] {method,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [7641] {method,
## model,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7642] {model,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [7643] {method,
## model,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [7644] {approach,
## dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7645] {approach,
## show,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7646] {show,
## dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7647] {approach,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7648] {approach,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7649] {dataset,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [7650] {approach,
## dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7651] {approach,
## model,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7652] {model,
## dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7653] {approach,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7654] {approach,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7655] {show,
## joint,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [7656] {approach,
## show,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7657] {approach,
## model,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7658] {model,
## show,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7659] {approach,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [7660] {approach,
## model,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7661] {model,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [7662] {show,
## joint,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [7663] {joint,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7664] {show,
## joint,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [7665] {show,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7666] {dataset,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7667] {show,
## joint,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [7668] {show,
## dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7669] {model,
## dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7670] {model,
## show,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7671] {model,
## show,
## dataset} => {joint} 0.1000000 0.7500000 5.625000 3
## [7672] {dataset,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [7673] {model,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7674] {model,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7675] {represent,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7676] {represent,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7677] {show,
## joint,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [7678] {show,
## joint,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [7679] {model,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7680] {model,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7681] {model,
## show,
## propos} => {joint} 0.1000000 0.7500000 5.625000 3
## [7682] {addit,
## imag,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [7683] {classif,
## addit,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [7684] {classif,
## imag,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [7685] {classif,
## addit,
## imag} => {studi} 0.1000000 1.0000000 7.500000 3
## [7686] {addit,
## imag,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [7687] {propos,
## addit,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [7688] {propos,
## imag,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [7689] {propos,
## addit,
## imag} => {studi} 0.1000000 1.0000000 7.500000 3
## [7690] {classif,
## addit,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [7691] {propos,
## addit,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [7692] {classif,
## propos,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [7693] {classif,
## propos,
## addit} => {studi} 0.1000000 1.0000000 7.500000 3
## [7694] {classif,
## imag,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [7695] {propos,
## imag,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [7696] {classif,
## propos,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [7697] {classif,
## propos,
## imag} => {studi} 0.1000000 1.0000000 7.500000 3
## [7698] {process,
## studi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [7699] {network,
## process,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [7700] {network,
## studi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [7701] {network,
## process,
## work} => {studi} 0.1000000 0.7500000 5.625000 3
## [7702] {evalu,
## make,
## paper} => {method} 0.1000000 1.0000000 2.727273 3
## [7703] {evalu,
## make,
## method} => {paper} 0.1000000 1.0000000 3.000000 3
## [7704] {evalu,
## method,
## paper} => {make} 0.1000000 1.0000000 3.333333 3
## [7705] {make,
## method,
## paper} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7706] {evalu,
## paper,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [7707] {evalu,
## network,
## paper} => {represent} 0.1000000 1.0000000 2.000000 3
## [7708] {evalu,
## network,
## represent} => {paper} 0.1000000 0.7500000 2.250000 3
## [7709] {network,
## paper,
## represent} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7710] {evalu,
## paper,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7711] {evalu,
## model,
## paper} => {show} 0.1000000 1.0000000 1.875000 3
## [7712] {evalu,
## model,
## show} => {paper} 0.1000000 0.7500000 2.250000 3
## [7713] {evalu,
## method,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [7714] {approach,
## evalu,
## method} => {task} 0.1000000 1.0000000 2.727273 3
## [7715] {approach,
## evalu,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [7716] {approach,
## method,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7717] {evalu,
## method,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [7718] {evalu,
## method,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [7719] {evalu,
## represent,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [7720] {method,
## represent,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7721] {evalu,
## method,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7722] {evalu,
## featur,
## method} => {task} 0.1000000 1.0000000 2.727273 3
## [7723] {evalu,
## featur,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [7724] {featur,
## method,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7725] {evalu,
## method,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [7726] {evalu,
## method,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [7727] {evalu,
## network,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [7728] {method,
## network,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7729] {approach,
## evalu,
## method} => {represent} 0.1000000 1.0000000 2.000000 3
## [7730] {evalu,
## method,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [7731] {approach,
## evalu,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [7732] {approach,
## method,
## represent} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7733] {approach,
## evalu,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [7734] {evalu,
## featur,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [7735] {approach,
## evalu,
## featur} => {method} 0.1000000 1.0000000 2.727273 3
## [7736] {approach,
## evalu,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [7737] {evalu,
## method,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [7738] {approach,
## evalu,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [7739] {data,
## evalu,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [7740] {evalu,
## method,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [7741] {data,
## evalu,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [7742] {data,
## method,
## show} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7743] {data,
## evalu,
## method} => {model} 0.1000000 1.0000000 1.875000 3
## [7744] {evalu,
## method,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [7745] {data,
## evalu,
## model} => {method} 0.1000000 1.0000000 2.727273 3
## [7746] {data,
## method,
## model} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7747] {evalu,
## method,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [7748] {evalu,
## featur,
## method} => {represent} 0.1000000 1.0000000 2.000000 3
## [7749] {evalu,
## featur,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [7750] {featur,
## method,
## represent} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7751] {evalu,
## method,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [7752] {evalu,
## method,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [7753] {evalu,
## network,
## represent} => {method} 0.1000000 0.7500000 2.045455 3
## [7754] {method,
## network,
## represent} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7755] {evalu,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7756] {evalu,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [7757] {evalu,
## model,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [7758] {evalu,
## featur,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [7759] {evalu,
## method,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [7760] {evalu,
## featur,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [7761] {approach,
## evalu,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [7762] {evalu,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [7763] {approach,
## evalu,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [7764] {approach,
## evalu,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7765] {evalu,
## featur,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [7766] {approach,
## evalu,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [7767] {approach,
## evalu,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [7768] {evalu,
## network,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [7769] {approach,
## evalu,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [7770] {approach,
## network,
## task} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7771] {evalu,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7772] {evalu,
## featur,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [7773] {evalu,
## featur,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [7774] {evalu,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [7775] {evalu,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [7776] {evalu,
## network,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [7777] {evalu,
## featur,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [7778] {evalu,
## network,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7779] {evalu,
## featur,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [7780] {approach,
## evalu,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [7781] {approach,
## evalu,
## featur} => {represent} 0.1000000 1.0000000 2.000000 3
## [7782] {evalu,
## featur,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [7783] {approach,
## evalu,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [7784] {approach,
## evalu,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [7785] {evalu,
## network,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [7786] {approach,
## evalu,
## featur} => {network} 0.1000000 1.0000000 1.578947 3
## [7787] {approach,
## evalu,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [7788] {evalu,
## featur,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [7789] {data,
## evalu,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7790] {data,
## evalu,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [7791] {evalu,
## model,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [7792] {evalu,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7793] {evalu,
## model,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [7794] {evalu,
## model,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [7795] {evalu,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [7796] {evalu,
## network,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [7797] {evalu,
## network,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [7798] {evalu,
## model,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [7799] {evalu,
## network,
## represent} => {model} 0.1000000 0.7500000 1.406250 3
## [7800] {evalu,
## model,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [7801] {model,
## network,
## represent} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7802] {evalu,
## featur,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [7803] {evalu,
## network,
## represent} => {featur} 0.1000000 0.7500000 1.406250 3
## [7804] {evalu,
## featur,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [7805] {evalu,
## model,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [7806] {evalu,
## network,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7807] {evalu,
## model,
## network} => {show} 0.1000000 1.0000000 1.875000 3
## [7808] {outperform,
## object,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [7809] {outperform,
## show,
## challeng} => {object} 0.1000000 1.0000000 3.750000 3
## [7810] {outperform,
## show,
## object} => {challeng} 0.1000000 1.0000000 6.000000 3
## [7811] {show,
## object,
## challeng} => {outperform} 0.1000000 1.0000000 7.500000 3
## [7812] {outperform,
## object,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [7813] {outperform,
## propos,
## challeng} => {object} 0.1000000 1.0000000 3.750000 3
## [7814] {outperform,
## object,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [7815] {object,
## propos,
## challeng} => {outperform} 0.1000000 1.0000000 7.500000 3
## [7816] {outperform,
## show,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [7817] {outperform,
## propos,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [7818] {outperform,
## show,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [7819] {show,
## propos,
## challeng} => {outperform} 0.1000000 1.0000000 7.500000 3
## [7820] {outperform,
## show,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [7821] {outperform,
## object,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7822] {outperform,
## show,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [7823] {data,
## outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [7824] {outperform,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [7825] {data,
## outperform,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [7826] {data,
## outperform,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [7827] {model,
## outperform,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [7828] {data,
## model,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [7829] {data,
## outperform,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7830] {featur,
## outperform,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [7831] {data,
## featur,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [7832] {outperform,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [7833] {model,
## outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [7834] {model,
## outperform,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [7835] {model,
## show,
## task} => {outperform} 0.1000000 0.7500000 5.625000 3
## [7836] {outperform,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7837] {featur,
## outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [7838] {featur,
## outperform,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [7839] {model,
## outperform,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7840] {featur,
## outperform,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [7841] {featur,
## model,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [7842] {data,
## outperform,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7843] {data,
## model,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [7844] {model,
## outperform,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [7845] {data,
## outperform,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [7846] {data,
## featur,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [7847] {featur,
## outperform,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [7848] {data,
## model,
## outperform} => {featur} 0.1000000 1.0000000 1.875000 3
## [7849] {data,
## featur,
## outperform} => {model} 0.1000000 1.0000000 1.875000 3
## [7850] {featur,
## model,
## outperform} => {data} 0.1000000 1.0000000 2.307692 3
## [7851] {model,
## outperform,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [7852] {featur,
## outperform,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7853] {featur,
## model,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [7854] {data,
## task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7855] {task,
## dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7856] {data,
## dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7857] {data,
## task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7858] {task,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7859] {data,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7860] {data,
## task,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7861] {featur,
## task,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7862] {data,
## featur,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7863] {data,
## task,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7864] {network,
## task,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7865] {data,
## network,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7866] {task,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7867] {task,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7868] {dataset,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7869] {task,
## dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7870] {featur,
## task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7871] {featur,
## dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7872] {featur,
## task,
## dataset} => {design} 0.1000000 0.7500000 5.625000 3
## [7873] {task,
## dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7874] {network,
## task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7875] {network,
## dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7876] {network,
## task,
## dataset} => {design} 0.1000000 0.7500000 5.625000 3
## [7877] {task,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7878] {featur,
## task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7879] {featur,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7880] {task,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7881] {network,
## task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7882] {network,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7883] {featur,
## task,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7884] {network,
## task,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7885] {featur,
## network,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7886] {data,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7887] {data,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7888] {dataset,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7889] {data,
## dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7890] {data,
## featur,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7891] {featur,
## dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7892] {data,
## dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7893] {data,
## network,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7894] {network,
## dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7895] {data,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7896] {data,
## featur,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7897] {featur,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7898] {data,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7899] {data,
## network,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7900] {network,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7901] {data,
## network,
## learn} => {design} 0.1000000 0.7500000 5.625000 3
## [7902] {data,
## featur,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7903] {data,
## network,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7904] {featur,
## network,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7905] {dataset,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7906] {featur,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7907] {featur,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7908] {dataset,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7909] {network,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7910] {network,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7911] {network,
## dataset,
## learn} => {design} 0.1000000 0.7500000 5.625000 3
## [7912] {featur,
## dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7913] {network,
## dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7914] {featur,
## network,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7915] {featur,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7916] {network,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7917] {featur,
## network,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7918] {show,
## general,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [7919] {model,
## general,
## system} => {show} 0.1000000 1.0000000 1.875000 3
## [7920] {model,
## show,
## system} => {general} 0.1000000 1.0000000 5.000000 3
## [7921] {model,
## show,
## general} => {system} 0.1000000 1.0000000 6.000000 3
## [7922] {model,
## recognit,
## system} => {featur} 0.1000000 1.0000000 1.875000 3
## [7923] {featur,
## recognit,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [7924] {featur,
## model,
## system} => {recognit} 0.1000000 1.0000000 3.333333 3
## [7925] {featur,
## model,
## recognit} => {system} 0.1000000 0.7500000 4.500000 3
## [7926] {method,
## perform,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7927] {method,
## dataset,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7928] {dataset,
## perform,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [7929] {method,
## perform,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7930] {method,
## propos,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7931] {perform,
## propos,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [7932] {method,
## dataset,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7933] {method,
## propos,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7934] {dataset,
## propos,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [7935] {dataset,
## perform,
## system} => {propos} 0.1333333 1.0000000 2.000000 4
## [7936] {perform,
## propos,
## system} => {dataset} 0.1333333 1.0000000 2.307692 4
## [7937] {dataset,
## propos,
## system} => {perform} 0.1333333 1.0000000 2.142857 4
## [7938] {dataset,
## perform,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [7939] {model,
## perform,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7940] {model,
## dataset,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7941] {dataset,
## perform,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [7942] {featur,
## perform,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7943] {featur,
## dataset,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7944] {perform,
## propos,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [7945] {model,
## perform,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7946] {model,
## propos,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7947] {perform,
## propos,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [7948] {featur,
## perform,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7949] {featur,
## propos,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7950] {dataset,
## propos,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [7951] {model,
## dataset,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7952] {model,
## propos,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7953] {dataset,
## propos,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [7954] {featur,
## dataset,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7955] {featur,
## propos,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7956] {represent,
## system,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [7957] {model,
## system,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [7958] {model,
## represent,
## system} => {learn} 0.1000000 1.0000000 2.307692 3
## [7959] {method,
## detect,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [7960] {detect,
## propos,
## stateoftheart} => {method} 0.1000000 1.0000000 2.727273 3
## [7961] {method,
## detect,
## propos} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [7962] {method,
## propos,
## stateoftheart} => {detect} 0.1000000 1.0000000 6.000000 3
## [7963] {method,
## detect,
## stateoftheart} => {featur} 0.1000000 1.0000000 1.875000 3
## [7964] {featur,
## detect,
## stateoftheart} => {method} 0.1000000 1.0000000 2.727273 3
## [7965] {featur,
## method,
## detect} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [7966] {featur,
## method,
## stateoftheart} => {detect} 0.1000000 1.0000000 6.000000 3
## [7967] {detect,
## propos,
## stateoftheart} => {featur} 0.1000000 1.0000000 1.875000 3
## [7968] {featur,
## detect,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [7969] {featur,
## detect,
## propos} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [7970] {featur,
## propos,
## stateoftheart} => {detect} 0.1000000 1.0000000 6.000000 3
## [7971] {method,
## appli,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [7972] {appli,
## detect,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [7973] {method,
## detect,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [7974] {method,
## appli,
## perform} => {detect} 0.1000000 0.7500000 4.500000 3
## [7975] {method,
## appli,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [7976] {appli,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [7977] {method,
## detect,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [7978] {method,
## appli,
## propos} => {detect} 0.1000000 0.7500000 4.500000 3
## [7979] {method,
## appli,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [7980] {featur,
## appli,
## detect} => {method} 0.1000000 1.0000000 2.727273 3
## [7981] {featur,
## method,
## detect} => {appli} 0.1000000 0.7500000 3.750000 3
## [7982] {featur,
## method,
## appli} => {detect} 0.1000000 1.0000000 6.000000 3
## [7983] {appli,
## detect,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [7984] {appli,
## detect,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [7985] {detect,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [7986] {appli,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [7987] {featur,
## appli,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [7988] {featur,
## detect,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [7989] {featur,
## appli,
## perform} => {detect} 0.1000000 0.7500000 4.500000 3
## [7990] {appli,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [7991] {featur,
## appli,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [7992] {featur,
## detect,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [7993] {featur,
## appli,
## propos} => {detect} 0.1000000 0.7500000 4.500000 3
## [7994] {method,
## detect,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [7995] {show,
## detect,
## object} => {method} 0.1000000 1.0000000 2.727273 3
## [7996] {method,
## show,
## detect} => {object} 0.1000000 1.0000000 3.750000 3
## [7997] {method,
## show,
## object} => {detect} 0.1000000 0.7500000 4.500000 3
## [7998] {method,
## detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [7999] {detect,
## object,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [8000] {method,
## detect,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [8001] {method,
## object,
## propos} => {detect} 0.1000000 1.0000000 6.000000 3
## [8002] {method,
## detect,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [8003] {featur,
## detect,
## object} => {method} 0.1000000 1.0000000 2.727273 3
## [8004] {featur,
## method,
## detect} => {object} 0.1000000 0.7500000 2.812500 3
## [8005] {featur,
## method,
## object} => {detect} 0.1000000 1.0000000 6.000000 3
## [8006] {show,
## detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [8007] {detect,
## object,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [8008] {show,
## detect,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [8009] {show,
## detect,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [8010] {featur,
## detect,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [8011] {featur,
## show,
## detect} => {object} 0.1000000 1.0000000 3.750000 3
## [8012] {featur,
## show,
## object} => {detect} 0.1000000 0.7500000 4.500000 3
## [8013] {detect,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [8014] {featur,
## detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [8015] {featur,
## detect,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [8016] {architectur,
## detect,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8017] {architectur,
## dataset,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [8018] {dataset,
## detect,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8019] {architectur,
## dataset,
## perform} => {detect} 0.1000000 0.7500000 4.500000 3
## [8020] {architectur,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [8021] {featur,
## architectur,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [8022] {featur,
## detect,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8023] {featur,
## architectur,
## perform} => {detect} 0.1000000 1.0000000 6.000000 3
## [8024] {architectur,
## detect,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [8025] {network,
## architectur,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [8026] {network,
## detect,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8027] {architectur,
## dataset,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [8028] {featur,
## architectur,
## detect} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8029] {featur,
## dataset,
## detect} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8030] {featur,
## architectur,
## dataset} => {detect} 0.1000000 0.7500000 4.500000 3
## [8031] {architectur,
## dataset,
## detect} => {network} 0.1000000 1.0000000 1.578947 3
## [8032] {network,
## architectur,
## detect} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8033] {network,
## dataset,
## detect} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8034] {featur,
## architectur,
## detect} => {network} 0.1000000 1.0000000 1.578947 3
## [8035] {network,
## architectur,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [8036] {featur,
## network,
## detect} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8037] {method,
## detect,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [8038] {method,
## detect,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [8039] {detect,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [8040] {method,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [8041] {featur,
## method,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [8042] {featur,
## detect,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [8043] {featur,
## method,
## perform} => {detect} 0.1000000 0.7500000 4.500000 3
## [8044] {method,
## dataset,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [8045] {method,
## detect,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8046] {dataset,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [8047] {method,
## dataset,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [8048] {featur,
## method,
## detect} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8049] {featur,
## dataset,
## detect} => {method} 0.1000000 0.7500000 2.045455 3
## [8050] {featur,
## method,
## dataset} => {detect} 0.1000000 0.7500000 4.500000 3
## [8051] {method,
## dataset,
## detect} => {network} 0.1000000 1.0000000 1.578947 3
## [8052] {method,
## network,
## detect} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8053] {network,
## dataset,
## detect} => {method} 0.1000000 0.7500000 2.045455 3
## [8054] {method,
## network,
## dataset} => {detect} 0.1000000 0.7500000 4.500000 3
## [8055] {method,
## show,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [8056] {method,
## detect,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [8057] {show,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [8058] {method,
## show,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [8059] {featur,
## method,
## detect} => {show} 0.1000000 0.7500000 1.406250 3
## [8060] {featur,
## show,
## detect} => {method} 0.1000000 1.0000000 2.727273 3
## [8061] {method,
## detect,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [8062] {featur,
## method,
## detect} => {propos} 0.1333333 1.0000000 2.000000 4
## [8063] {featur,
## detect,
## propos} => {method} 0.1333333 1.0000000 2.727273 4
## [8064] {featur,
## method,
## propos} => {detect} 0.1333333 0.8000000 4.800000 4
## [8065] {method,
## detect,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [8066] {method,
## network,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [8067] {network,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [8068] {featur,
## method,
## detect} => {network} 0.1000000 0.7500000 1.184211 3
## [8069] {method,
## network,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [8070] {featur,
## network,
## detect} => {method} 0.1000000 0.7500000 2.045455 3
## [8071] {dataset,
## detect,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [8072] {featur,
## detect,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8073] {featur,
## dataset,
## detect} => {work} 0.1000000 0.7500000 1.875000 3
## [8074] {dataset,
## detect,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8075] {network,
## detect,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8076] {network,
## dataset,
## detect} => {work} 0.1000000 0.7500000 1.875000 3
## [8077] {featur,
## detect,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8078] {network,
## detect,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [8079] {featur,
## network,
## detect} => {work} 0.1000000 0.7500000 1.875000 3
## [8080] {dataset,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [8081] {featur,
## detect,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8082] {featur,
## dataset,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [8083] {dataset,
## detect,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [8084] {network,
## detect,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8085] {network,
## dataset,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [8086] {network,
## dataset,
## perform} => {detect} 0.1000000 0.7500000 4.500000 3
## [8087] {detect,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [8088] {featur,
## detect,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [8089] {featur,
## detect,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [8090] {featur,
## detect,
## perform} => {network} 0.1000000 0.7500000 1.184211 3
## [8091] {network,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [8092] {featur,
## network,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [8093] {featur,
## network,
## perform} => {detect} 0.1000000 1.0000000 6.000000 3
## [8094] {dataset,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [8095] {featur,
## dataset,
## detect} => {propos} 0.1000000 0.7500000 1.500000 3
## [8096] {featur,
## detect,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8097] {dataset,
## detect,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [8098] {network,
## dataset,
## detect} => {propos} 0.1000000 0.7500000 1.500000 3
## [8099] {network,
## detect,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8100] {featur,
## dataset,
## detect} => {network} 0.1333333 1.0000000 1.578947 4
## [8101] {network,
## dataset,
## detect} => {featur} 0.1333333 1.0000000 1.875000 4
## [8102] {featur,
## network,
## detect} => {dataset} 0.1333333 1.0000000 2.307692 4
## [8103] {show,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [8104] {featur,
## show,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [8105] {featur,
## detect,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [8106] {featur,
## show,
## propos} => {detect} 0.1000000 0.7500000 4.500000 3
## [8107] {featur,
## detect,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [8108] {network,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [8109] {featur,
## network,
## detect} => {propos} 0.1000000 0.7500000 1.500000 3
## [8110] {represent,
## object,
## stateoftheart} => {show} 0.1000000 1.0000000 1.875000 3
## [8111] {show,
## object,
## stateoftheart} => {represent} 0.1000000 1.0000000 2.000000 3
## [8112] {represent,
## show,
## stateoftheart} => {object} 0.1000000 1.0000000 3.750000 3
## [8113] {represent,
## show,
## object} => {stateoftheart} 0.1000000 1.0000000 6.000000 3
## [8114] {represent,
## object,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [8115] {object,
## propos,
## stateoftheart} => {represent} 0.1000000 1.0000000 2.000000 3
## [8116] {represent,
## propos,
## stateoftheart} => {object} 0.1000000 1.0000000 3.750000 3
## [8117] {represent,
## object,
## propos} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [8118] {show,
## object,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [8119] {object,
## propos,
## stateoftheart} => {show} 0.1000000 1.0000000 1.875000 3
## [8120] {show,
## propos,
## stateoftheart} => {object} 0.1000000 1.0000000 3.750000 3
## [8121] {method,
## propos,
## stateoftheart} => {featur} 0.1000000 1.0000000 1.875000 3
## [8122] {featur,
## method,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [8123] {featur,
## propos,
## stateoftheart} => {method} 0.1000000 1.0000000 2.727273 3
## [8124] {dataset,
## propos,
## stateoftheart} => {network} 0.1000000 1.0000000 1.578947 3
## [8125] {network,
## dataset,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [8126] {network,
## propos,
## stateoftheart} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8127] {represent,
## show,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [8128] {represent,
## propos,
## stateoftheart} => {show} 0.1000000 1.0000000 1.875000 3
## [8129] {show,
## propos,
## stateoftheart} => {represent} 0.1000000 1.0000000 2.000000 3
## [8130] {architectur,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8131] {addit,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8132] {architectur,
## effici,
## work} => {addit} 0.1000000 0.7500000 4.500000 3
## [8133] {architectur,
## addit,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [8134] {architectur,
## addit,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8135] {dataset,
## addit,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8136] {architectur,
## dataset,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [8137] {architectur,
## dataset,
## addit} => {effici} 0.1000000 1.0000000 6.000000 3
## [8138] {architectur,
## addit,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [8139] {propos,
## addit,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8140] {architectur,
## propos,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [8141] {architectur,
## propos,
## addit} => {effici} 0.1000000 1.0000000 6.000000 3
## [8142] {architectur,
## addit,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [8143] {network,
## addit,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8144] {network,
## architectur,
## effici} => {addit} 0.1000000 0.7500000 4.500000 3
## [8145] {network,
## architectur,
## addit} => {effici} 0.1000000 1.0000000 6.000000 3
## [8146] {addit,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8147] {dataset,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8148] {dataset,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8149] {dataset,
## addit,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [8150] {addit,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [8151] {propos,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8152] {propos,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8153] {propos,
## addit,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [8154] {addit,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8155] {network,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8156] {network,
## effici,
## work} => {addit} 0.1000000 0.7500000 4.500000 3
## [8157] {network,
## addit,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [8158] {dataset,
## addit,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [8159] {propos,
## addit,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8160] {dataset,
## propos,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [8161] {dataset,
## propos,
## addit} => {effici} 0.1000000 0.7500000 4.500000 3
## [8162] {dataset,
## addit,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [8163] {network,
## addit,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8164] {network,
## dataset,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [8165] {network,
## dataset,
## addit} => {effici} 0.1000000 0.7500000 4.500000 3
## [8166] {propos,
## addit,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [8167] {network,
## addit,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [8168] {network,
## propos,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [8169] {network,
## propos,
## addit} => {effici} 0.1000000 0.7500000 4.500000 3
## [8170] {reduc,
## comput,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [8171] {comput,
## effici,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [8172] {reduc,
## effici,
## optim} => {comput} 0.1000000 1.0000000 4.285714 3
## [8173] {reduc,
## comput,
## optim} => {effici} 0.1000000 1.0000000 6.000000 3
## [8174] {reduc,
## comput,
## effici} => {problem} 0.1000000 1.0000000 3.333333 3
## [8175] {comput,
## effici,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [8176] {reduc,
## effici,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [8177] {reduc,
## comput,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [8178] {reduc,
## comput,
## effici} => {improv} 0.1000000 1.0000000 3.333333 3
## [8179] {improv,
## comput,
## effici} => {reduc} 0.1000000 1.0000000 4.285714 3
## [8180] {reduc,
## improv,
## effici} => {comput} 0.1000000 1.0000000 4.285714 3
## [8181] {reduc,
## improv,
## comput} => {effici} 0.1000000 1.0000000 6.000000 3
## [8182] {comput,
## effici,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [8183] {comput,
## effici,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [8184] {effici,
## optim,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [8185] {comput,
## optim,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [8186] {comput,
## effici,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [8187] {improv,
## comput,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [8188] {improv,
## effici,
## optim} => {comput} 0.1000000 1.0000000 4.285714 3
## [8189] {improv,
## comput,
## optim} => {effici} 0.1000000 1.0000000 6.000000 3
## [8190] {comput,
## effici,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [8191] {improv,
## comput,
## effici} => {problem} 0.1000000 1.0000000 3.333333 3
## [8192] {improv,
## effici,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [8193] {improv,
## comput,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [8194] {reduc,
## effici,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [8195] {reduc,
## effici,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [8196] {effici,
## optim,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [8197] {reduc,
## optim,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [8198] {reduc,
## effici,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [8199] {reduc,
## improv,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [8200] {improv,
## effici,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [8201] {reduc,
## improv,
## optim} => {effici} 0.1000000 1.0000000 6.000000 3
## [8202] {reduc,
## effici,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [8203] {reduc,
## improv,
## effici} => {problem} 0.1000000 1.0000000 3.333333 3
## [8204] {improv,
## effici,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [8205] {reduc,
## improv,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [8206] {effici,
## optim,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [8207] {improv,
## effici,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [8208] {improv,
## effici,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [8209] {improv,
## optim,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [8210] {architectur,
## effici,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [8211] {architectur,
## perform,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8212] {perform,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8213] {architectur,
## perform,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [8214] {architectur,
## effici,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8215] {architectur,
## dataset,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8216] {dataset,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8217] {architectur,
## dataset,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [8218] {architectur,
## effici,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [8219] {architectur,
## propos,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8220] {propos,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8221] {architectur,
## propos,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [8222] {architectur,
## effici,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [8223] {network,
## architectur,
## effici} => {work} 0.1333333 1.0000000 2.500000 4
## [8224] {network,
## effici,
## work} => {architectur} 0.1333333 1.0000000 3.750000 4
## [8225] {network,
## architectur,
## work} => {effici} 0.1333333 0.8000000 4.800000 4
## [8226] {architectur,
## perform,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [8227] {network,
## architectur,
## effici} => {perform} 0.1000000 0.7500000 1.607143 3
## [8228] {network,
## perform,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8229] {architectur,
## dataset,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [8230] {architectur,
## propos,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8231] {dataset,
## propos,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8232] {architectur,
## dataset,
## propos} => {effici} 0.1000000 0.7500000 4.500000 3
## [8233] {architectur,
## dataset,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [8234] {network,
## architectur,
## effici} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8235] {network,
## dataset,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8236] {architectur,
## propos,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [8237] {network,
## architectur,
## effici} => {propos} 0.1000000 0.7500000 1.500000 3
## [8238] {network,
## propos,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8239] {perform,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8240] {network,
## effici,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [8241] {network,
## perform,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8242] {network,
## perform,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [8243] {dataset,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [8244] {propos,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8245] {dataset,
## propos,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8246] {dataset,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8247] {network,
## effici,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8248] {network,
## dataset,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8249] {propos,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8250] {network,
## effici,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [8251] {network,
## propos,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8252] {dataset,
## propos,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [8253] {network,
## dataset,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [8254] {network,
## propos,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8255] {addit,
## set,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8256] {dataset,
## addit,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [8257] {dataset,
## set,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8258] {dataset,
## addit,
## work} => {set} 0.1000000 0.7500000 5.625000 3
## [8259] {addit,
## set,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [8260] {propos,
## addit,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [8261] {propos,
## set,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8262] {propos,
## addit,
## work} => {set} 0.1000000 0.7500000 5.625000 3
## [8263] {addit,
## set,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8264] {network,
## addit,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [8265] {network,
## set,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8266] {network,
## addit,
## work} => {set} 0.1000000 0.7500000 5.625000 3
## [8267] {dataset,
## addit,
## set} => {propos} 0.1000000 1.0000000 2.000000 3
## [8268] {propos,
## addit,
## set} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8269] {dataset,
## propos,
## set} => {addit} 0.1000000 1.0000000 6.000000 3
## [8270] {dataset,
## propos,
## addit} => {set} 0.1000000 0.7500000 5.625000 3
## [8271] {dataset,
## addit,
## set} => {network} 0.1000000 1.0000000 1.578947 3
## [8272] {network,
## addit,
## set} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8273] {network,
## dataset,
## set} => {addit} 0.1000000 1.0000000 6.000000 3
## [8274] {network,
## dataset,
## addit} => {set} 0.1000000 0.7500000 5.625000 3
## [8275] {propos,
## addit,
## set} => {network} 0.1000000 1.0000000 1.578947 3
## [8276] {network,
## addit,
## set} => {propos} 0.1000000 1.0000000 2.000000 3
## [8277] {network,
## propos,
## set} => {addit} 0.1000000 1.0000000 6.000000 3
## [8278] {network,
## propos,
## addit} => {set} 0.1000000 0.7500000 5.625000 3
## [8279] {task,
## recognit,
## set} => {data} 0.1000000 1.0000000 2.307692 3
## [8280] {data,
## recognit,
## set} => {task} 0.1000000 1.0000000 2.727273 3
## [8281] {data,
## task,
## set} => {recognit} 0.1000000 1.0000000 3.333333 3
## [8282] {data,
## task,
## recognit} => {set} 0.1000000 0.7500000 5.625000 3
## [8283] {task,
## recognit,
## set} => {learn} 0.1000000 1.0000000 2.307692 3
## [8284] {recognit,
## set,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [8285] {task,
## set,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [8286] {task,
## recognit,
## learn} => {set} 0.1000000 0.7500000 5.625000 3
## [8287] {data,
## recognit,
## set} => {learn} 0.1000000 1.0000000 2.307692 3
## [8288] {recognit,
## set,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [8289] {data,
## set,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [8290] {data,
## task,
## set} => {learn} 0.1000000 1.0000000 2.307692 3
## [8291] {task,
## set,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [8292] {data,
## set,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [8293] {dataset,
## set,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [8294] {propos,
## set,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8295] {dataset,
## propos,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [8296] {dataset,
## set,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8297] {network,
## set,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8298] {network,
## dataset,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [8299] {propos,
## set,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8300] {network,
## set,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [8301] {network,
## propos,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [8302] {dataset,
## propos,
## set} => {network} 0.1000000 1.0000000 1.578947 3
## [8303] {network,
## dataset,
## set} => {propos} 0.1000000 1.0000000 2.000000 3
## [8304] {network,
## propos,
## set} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8305] {architectur,
## result,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [8306] {architectur,
## neural,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [8307] {neural,
## result,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8308] {architectur,
## neural,
## result} => {shallow} 0.1000000 1.0000000 6.000000 3
## [8309] {architectur,
## result,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [8310] {featur,
## architectur,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [8311] {featur,
## result,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8312] {featur,
## architectur,
## result} => {shallow} 0.1000000 0.7500000 4.500000 3
## [8313] {architectur,
## result,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [8314] {network,
## architectur,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [8315] {network,
## result,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8316] {network,
## architectur,
## result} => {shallow} 0.1000000 1.0000000 6.000000 3
## [8317] {architectur,
## neural,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [8318] {featur,
## architectur,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [8319] {featur,
## neural,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8320] {featur,
## architectur,
## neural} => {shallow} 0.1000000 1.0000000 6.000000 3
## [8321] {architectur,
## neural,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [8322] {network,
## architectur,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [8323] {network,
## neural,
## shallow} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8324] {network,
## architectur,
## neural} => {shallow} 0.1000000 0.7500000 4.500000 3
## [8325] {featur,
## architectur,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [8326] {network,
## architectur,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [8327] {featur,
## network,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8328] {classif,
## method,
## shallow} => {approach} 0.1000000 1.0000000 2.500000 3
## [8329] {approach,
## classif,
## shallow} => {method} 0.1000000 1.0000000 2.727273 3
## [8330] {approach,
## method,
## shallow} => {classif} 0.1000000 1.0000000 3.750000 3
## [8331] {classif,
## method,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [8332] {classif,
## featur,
## shallow} => {method} 0.1000000 1.0000000 2.727273 3
## [8333] {featur,
## method,
## shallow} => {classif} 0.1000000 1.0000000 3.750000 3
## [8334] {approach,
## classif,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [8335] {classif,
## featur,
## shallow} => {approach} 0.1000000 1.0000000 2.500000 3
## [8336] {approach,
## featur,
## shallow} => {classif} 0.1000000 1.0000000 3.750000 3
## [8337] {approach,
## classif,
## featur} => {shallow} 0.1000000 0.7500000 4.500000 3
## [8338] {improv,
## perform,
## shallow} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8339] {dataset,
## improv,
## shallow} => {perform} 0.1000000 1.0000000 2.142857 3
## [8340] {dataset,
## perform,
## shallow} => {improv} 0.1000000 1.0000000 3.333333 3
## [8341] {improv,
## perform,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [8342] {featur,
## improv,
## shallow} => {perform} 0.1000000 1.0000000 2.142857 3
## [8343] {featur,
## perform,
## shallow} => {improv} 0.1000000 1.0000000 3.333333 3
## [8344] {featur,
## improv,
## perform} => {shallow} 0.1000000 0.7500000 4.500000 3
## [8345] {dataset,
## improv,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [8346] {featur,
## improv,
## shallow} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8347] {featur,
## dataset,
## shallow} => {improv} 0.1000000 1.0000000 3.333333 3
## [8348] {featur,
## dataset,
## improv} => {shallow} 0.1000000 0.7500000 4.500000 3
## [8349] {neural,
## result,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [8350] {featur,
## result,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [8351] {featur,
## neural,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [8352] {featur,
## neural,
## result} => {shallow} 0.1000000 1.0000000 6.000000 3
## [8353] {neural,
## result,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [8354] {network,
## result,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [8355] {network,
## neural,
## shallow} => {result} 0.1000000 0.7500000 2.250000 3
## [8356] {featur,
## result,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [8357] {network,
## result,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [8358] {featur,
## network,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [8359] {featur,
## network,
## result} => {shallow} 0.1000000 0.7500000 4.500000 3
## [8360] {featur,
## neural,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [8361] {network,
## neural,
## shallow} => {featur} 0.1000000 0.7500000 1.406250 3
## [8362] {featur,
## network,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [8363] {featur,
## network,
## neural} => {shallow} 0.1000000 0.7500000 4.500000 3
## [8364] {approach,
## method,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [8365] {featur,
## method,
## shallow} => {approach} 0.1000000 1.0000000 2.500000 3
## [8366] {approach,
## featur,
## shallow} => {method} 0.1000000 1.0000000 2.727273 3
## [8367] {dataset,
## perform,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [8368] {featur,
## perform,
## shallow} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8369] {featur,
## dataset,
## shallow} => {perform} 0.1000000 1.0000000 2.142857 3
## [8370] {work,
## semant,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [8371] {represent,
## semant,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [8372] {represent,
## work,
## semant} => {larg} 0.1000000 0.7500000 4.500000 3
## [8373] {represent,
## work,
## larg} => {semant} 0.1000000 0.7500000 4.500000 3
## [8374] {work,
## semant,
## larg} => {propos} 0.1000000 1.0000000 2.000000 3
## [8375] {propos,
## semant,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [8376] {propos,
## work,
## semant} => {larg} 0.1000000 0.7500000 4.500000 3
## [8377] {propos,
## work,
## larg} => {semant} 0.1000000 1.0000000 6.000000 3
## [8378] {represent,
## semant,
## larg} => {propos} 0.1000000 1.0000000 2.000000 3
## [8379] {propos,
## semant,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [8380] {represent,
## propos,
## semant} => {larg} 0.1000000 0.7500000 4.500000 3
## [8381] {represent,
## propos,
## larg} => {semant} 0.1000000 1.0000000 6.000000 3
## [8382] {approach,
## task,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8383] {task,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8384] {approach,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8385] {approach,
## task,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [8386] {approach,
## task,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8387] {data,
## task,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8388] {approach,
## data,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8389] {approach,
## task,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8390] {task,
## dataset,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8391] {approach,
## dataset,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8392] {approach,
## task,
## dataset} => {semant} 0.1000000 1.0000000 6.000000 3
## [8393] {approach,
## task,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8394] {task,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8395] {approach,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8396] {approach,
## task,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [8397] {approach,
## task,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8398] {represent,
## task,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8399] {approach,
## represent,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8400] {approach,
## task,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8401] {task,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8402] {approach,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8403] {task,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8404] {data,
## task,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8405] {data,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8406] {data,
## task,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8407] {task,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8408] {task,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8409] {dataset,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8410] {task,
## dataset,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8411] {task,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8412] {task,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8413] {work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8414] {task,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [8415] {task,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8416] {represent,
## task,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8417] {represent,
## work,
## semant} => {task} 0.1000000 0.7500000 2.045455 3
## [8418] {represent,
## task,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [8419] {task,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8420] {task,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8421] {propos,
## work,
## semant} => {task} 0.1000000 0.7500000 2.045455 3
## [8422] {task,
## propos,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8423] {data,
## task,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8424] {task,
## dataset,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8425] {data,
## dataset,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8426] {data,
## task,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8427] {task,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8428] {data,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8429] {data,
## task,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8430] {represent,
## task,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8431] {data,
## represent,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8432] {data,
## task,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8433] {task,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8434] {data,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8435] {task,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8436] {task,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8437] {dataset,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8438] {task,
## dataset,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8439] {represent,
## task,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8440] {represent,
## dataset,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8441] {represent,
## task,
## dataset} => {semant} 0.1000000 0.7500000 4.500000 3
## [8442] {task,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8443] {task,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8444] {dataset,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8445] {task,
## dataset,
## propos} => {semant} 0.1000000 0.7500000 4.500000 3
## [8446] {task,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8447] {represent,
## task,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8448] {represent,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8449] {task,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8450] {task,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8451] {propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [8452] {represent,
## task,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8453] {task,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8454] {represent,
## propos,
## semant} => {task} 0.1000000 0.7500000 2.045455 3
## [8455] {approach,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8456] {approach,
## data,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8457] {data,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8458] {approach,
## data,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [8459] {approach,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8460] {approach,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8461] {dataset,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8462] {approach,
## dataset,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8463] {approach,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8464] {approach,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8465] {work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8466] {approach,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [8467] {approach,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8468] {approach,
## represent,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8469] {represent,
## work,
## semant} => {approach} 0.1000000 0.7500000 1.875000 3
## [8470] {approach,
## represent,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8471] {approach,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8472] {approach,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8473] {propos,
## work,
## semant} => {approach} 0.1000000 0.7500000 1.875000 3
## [8474] {approach,
## propos,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8475] {approach,
## data,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8476] {approach,
## dataset,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8477] {data,
## dataset,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8478] {approach,
## data,
## dataset} => {semant} 0.1000000 1.0000000 6.000000 3
## [8479] {approach,
## data,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8480] {approach,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8481] {data,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8482] {approach,
## data,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [8483] {approach,
## data,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8484] {approach,
## represent,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8485] {data,
## represent,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8486] {approach,
## data,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8487] {approach,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8488] {data,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8489] {approach,
## data,
## propos} => {semant} 0.1000000 0.7500000 4.500000 3
## [8490] {approach,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8491] {approach,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8492] {dataset,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8493] {approach,
## dataset,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8494] {approach,
## represent,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8495] {represent,
## dataset,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8496] {approach,
## represent,
## dataset} => {semant} 0.1000000 0.7500000 4.500000 3
## [8497] {approach,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8498] {approach,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8499] {dataset,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8500] {approach,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8501] {approach,
## represent,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8502] {represent,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8503] {approach,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8504] {approach,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8505] {propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8506] {approach,
## represent,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8507] {approach,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8508] {represent,
## propos,
## semant} => {approach} 0.1000000 0.7500000 1.875000 3
## [8509] {data,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8510] {dataset,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8511] {data,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8512] {data,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8513] {work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8514] {data,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8515] {data,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [8516] {data,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8517] {represent,
## work,
## semant} => {data} 0.1000000 0.7500000 1.730769 3
## [8518] {data,
## represent,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8519] {data,
## represent,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8520] {data,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8521] {propos,
## work,
## semant} => {data} 0.1000000 0.7500000 1.730769 3
## [8522] {data,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8523] {data,
## propos,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8524] {dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8525] {work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8526] {dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8527] {dataset,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [8528] {dataset,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8529] {represent,
## work,
## semant} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8530] {represent,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8531] {represent,
## dataset,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8532] {dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8533] {propos,
## work,
## semant} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8534] {dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8535] {work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8536] {represent,
## work,
## semant} => {learn} 0.1000000 0.7500000 1.730769 3
## [8537] {represent,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8538] {represent,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [8539] {work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8540] {propos,
## work,
## semant} => {learn} 0.1000000 0.7500000 1.730769 3
## [8541] {propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8542] {propos,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [8543] {represent,
## work,
## semant} => {show} 0.1000000 0.7500000 1.406250 3
## [8544] {show,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8545] {represent,
## show,
## semant} => {work} 0.1000000 0.7500000 1.875000 3
## [8546] {represent,
## show,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8547] {represent,
## work,
## semant} => {propos} 0.1333333 1.0000000 2.000000 4
## [8548] {propos,
## work,
## semant} => {represent} 0.1333333 1.0000000 2.000000 4
## [8549] {represent,
## propos,
## semant} => {work} 0.1333333 1.0000000 2.500000 4
## [8550] {represent,
## propos,
## work} => {semant} 0.1333333 1.0000000 6.000000 4
## [8551] {show,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8552] {propos,
## work,
## semant} => {show} 0.1000000 0.7500000 1.406250 3
## [8553] {show,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8554] {show,
## propos,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8555] {data,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8556] {data,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8557] {dataset,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8558] {data,
## dataset,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8559] {data,
## represent,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8560] {represent,
## dataset,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8561] {data,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8562] {data,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8563] {dataset,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8564] {data,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8565] {data,
## represent,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8566] {represent,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8567] {data,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8568] {data,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8569] {propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8570] {data,
## represent,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8571] {data,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8572] {represent,
## propos,
## semant} => {data} 0.1000000 0.7500000 1.730769 3
## [8573] {dataset,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8574] {represent,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8575] {represent,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8576] {dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8577] {dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8578] {propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8579] {represent,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8580] {dataset,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8581] {represent,
## propos,
## semant} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8582] {represent,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [8583] {propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8584] {represent,
## propos,
## semant} => {learn} 0.1000000 0.7500000 1.730769 3
## [8585] {represent,
## show,
## semant} => {propos} 0.1000000 0.7500000 1.500000 3
## [8586] {represent,
## propos,
## semant} => {show} 0.1000000 0.7500000 1.406250 3
## [8587] {show,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8588] {represent,
## show,
## semant} => {network} 0.1000000 0.7500000 1.184211 3
## [8589] {network,
## represent,
## semant} => {show} 0.1000000 1.0000000 1.875000 3
## [8590] {network,
## show,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8591] {architectur,
## process,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [8592] {process,
## analysi,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8593] {architectur,
## analysi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [8594] {architectur,
## process,
## work} => {analysi} 0.1000000 1.0000000 7.500000 3
## [8595] {architectur,
## process,
## analysi} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8596] {dataset,
## process,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8597] {architectur,
## dataset,
## analysi} => {process} 0.1000000 1.0000000 5.000000 3
## [8598] {architectur,
## dataset,
## process} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8599] {architectur,
## process,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8600] {network,
## process,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8601] {network,
## architectur,
## analysi} => {process} 0.1000000 0.7500000 3.750000 3
## [8602] {network,
## architectur,
## process} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8603] {process,
## analysi,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8604] {dataset,
## process,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [8605] {dataset,
## analysi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [8606] {dataset,
## process,
## work} => {analysi} 0.1000000 1.0000000 7.500000 3
## [8607] {process,
## analysi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8608] {network,
## process,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [8609] {network,
## analysi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [8610] {network,
## process,
## work} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8611] {dataset,
## process,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8612] {network,
## process,
## analysi} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8613] {network,
## dataset,
## analysi} => {process} 0.1000000 1.0000000 5.000000 3
## [8614] {network,
## dataset,
## process} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8615] {architectur,
## experi,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [8616] {classif,
## experi,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8617] {classif,
## architectur,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [8618] {classif,
## architectur,
## experi} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8619] {architectur,
## experi,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [8620] {experi,
## propos,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8621] {architectur,
## propos,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [8622] {architectur,
## experi,
## propos} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8623] {architectur,
## experi,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8624] {network,
## experi,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8625] {network,
## architectur,
## analysi} => {experi} 0.1000000 0.7500000 2.812500 3
## [8626] {network,
## architectur,
## experi} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8627] {classif,
## experi,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [8628] {experi,
## propos,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [8629] {classif,
## propos,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [8630] {classif,
## experi,
## propos} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8631] {classif,
## experi,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8632] {network,
## experi,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [8633] {classif,
## network,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [8634] {classif,
## network,
## experi} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8635] {experi,
## propos,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8636] {network,
## experi,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [8637] {network,
## propos,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [8638] {network,
## experi,
## propos} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8639] {classif,
## architectur,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [8640] {architectur,
## propos,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [8641] {classif,
## propos,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8642] {classif,
## architectur,
## propos} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8643] {classif,
## architectur,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8644] {network,
## architectur,
## analysi} => {classif} 0.1000000 0.7500000 2.812500 3
## [8645] {classif,
## network,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8646] {classif,
## network,
## architectur} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8647] {architectur,
## neural,
## analysi} => {train} 0.1000000 1.0000000 2.500000 3
## [8648] {train,
## architectur,
## analysi} => {neural} 0.1000000 1.0000000 3.000000 3
## [8649] {train,
## neural,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8650] {train,
## architectur,
## neural} => {analysi} 0.1000000 1.0000000 7.500000 3
## [8651] {architectur,
## neural,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8652] {network,
## architectur,
## analysi} => {neural} 0.1000000 0.7500000 2.250000 3
## [8653] {network,
## neural,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8654] {network,
## architectur,
## neural} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8655] {train,
## architectur,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8656] {network,
## architectur,
## analysi} => {train} 0.1000000 0.7500000 1.875000 3
## [8657] {network,
## train,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8658] {network,
## train,
## architectur} => {analysi} 0.1000000 1.0000000 7.500000 3
## [8659] {architectur,
## analysi,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8660] {architectur,
## dataset,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [8661] {dataset,
## analysi,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8662] {architectur,
## dataset,
## work} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8663] {architectur,
## analysi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8664] {network,
## architectur,
## analysi} => {work} 0.1000000 0.7500000 1.875000 3
## [8665] {network,
## analysi,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8666] {architectur,
## dataset,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8667] {network,
## architectur,
## analysi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8668] {network,
## dataset,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8669] {architectur,
## propos,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8670] {network,
## architectur,
## analysi} => {propos} 0.1000000 0.7500000 1.500000 3
## [8671] {network,
## propos,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8672] {featur,
## architectur,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8673] {network,
## architectur,
## analysi} => {featur} 0.1000000 0.7500000 1.406250 3
## [8674] {featur,
## network,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8675] {classif,
## propos,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8676] {classif,
## network,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [8677] {network,
## propos,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [8678] {classif,
## network,
## propos} => {analysi} 0.1000000 0.7500000 5.625000 3
## [8679] {train,
## neural,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8680] {network,
## neural,
## analysi} => {train} 0.1000000 1.0000000 2.500000 3
## [8681] {network,
## train,
## analysi} => {neural} 0.1000000 1.0000000 3.000000 3
## [8682] {dataset,
## analysi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8683] {network,
## analysi,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8684] {network,
## dataset,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [8685] {method,
## appli,
## addit} => {perform} 0.1000000 1.0000000 2.142857 3
## [8686] {appli,
## perform,
## addit} => {method} 0.1000000 1.0000000 2.727273 3
## [8687] {method,
## perform,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [8688] {method,
## appli,
## perform} => {addit} 0.1000000 0.7500000 4.500000 3
## [8689] {method,
## appli,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8690] {appli,
## propos,
## addit} => {method} 0.1000000 0.7500000 2.045455 3
## [8691] {method,
## propos,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [8692] {method,
## appli,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [8693] {appli,
## addit,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8694] {appli,
## dataset,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [8695] {dataset,
## addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [8696] {appli,
## dataset,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8697] {appli,
## addit,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [8698] {appli,
## propos,
## addit} => {work} 0.1000000 0.7500000 1.875000 3
## [8699] {propos,
## addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [8700] {appli,
## propos,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8701] {appli,
## addit,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8702] {network,
## appli,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [8703] {network,
## addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [8704] {network,
## appli,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8705] {appli,
## perform,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8706] {appli,
## propos,
## addit} => {perform} 0.1000000 0.7500000 1.607143 3
## [8707] {perform,
## propos,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [8708] {appli,
## dataset,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8709] {appli,
## propos,
## addit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8710] {dataset,
## propos,
## addit} => {appli} 0.1000000 0.7500000 3.750000 3
## [8711] {appli,
## dataset,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [8712] {appli,
## dataset,
## addit} => {network} 0.1000000 1.0000000 1.578947 3
## [8713] {network,
## appli,
## addit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8714] {network,
## dataset,
## addit} => {appli} 0.1000000 0.7500000 3.750000 3
## [8715] {network,
## appli,
## dataset} => {addit} 0.1000000 0.7500000 4.500000 3
## [8716] {show,
## appli,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8717] {appli,
## propos,
## addit} => {show} 0.1000000 0.7500000 1.406250 3
## [8718] {show,
## propos,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [8719] {show,
## appli,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [8720] {appli,
## propos,
## addit} => {network} 0.1000000 0.7500000 1.184211 3
## [8721] {network,
## appli,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8722] {network,
## propos,
## addit} => {appli} 0.1000000 0.7500000 3.750000 3
## [8723] {network,
## appli,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [8724] {classif,
## addit,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [8725] {propos,
## addit,
## imag} => {classif} 0.1000000 1.0000000 3.750000 3
## [8726] {classif,
## propos,
## addit} => {imag} 0.1000000 1.0000000 6.000000 3
## [8727] {classif,
## propos,
## imag} => {addit} 0.1000000 1.0000000 6.000000 3
## [8728] {architectur,
## addit,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8729] {architectur,
## dataset,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [8730] {dataset,
## addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8731] {architectur,
## dataset,
## work} => {addit} 0.1000000 0.7500000 4.500000 3
## [8732] {architectur,
## addit,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [8733] {architectur,
## propos,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [8734] {propos,
## addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8735] {architectur,
## propos,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8736] {architectur,
## addit,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8737] {network,
## architectur,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [8738] {network,
## addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8739] {architectur,
## dataset,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8740] {architectur,
## propos,
## addit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8741] {dataset,
## propos,
## addit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8742] {architectur,
## dataset,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [8743] {architectur,
## dataset,
## addit} => {network} 0.1000000 1.0000000 1.578947 3
## [8744] {network,
## architectur,
## addit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8745] {network,
## dataset,
## addit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8746] {architectur,
## propos,
## addit} => {network} 0.1000000 1.0000000 1.578947 3
## [8747] {network,
## architectur,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8748] {network,
## propos,
## addit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8749] {recognit,
## addit,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [8750] {propos,
## recognit,
## addit} => {learn} 0.1000000 1.0000000 2.307692 3
## [8751] {propos,
## addit,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [8752] {propos,
## recognit,
## learn} => {addit} 0.1000000 0.7500000 4.500000 3
## [8753] {method,
## perform,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8754] {method,
## propos,
## addit} => {perform} 0.1000000 1.0000000 2.142857 3
## [8755] {perform,
## propos,
## addit} => {method} 0.1000000 1.0000000 2.727273 3
## [8756] {dataset,
## addit,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [8757] {propos,
## addit,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [8758] {dataset,
## propos,
## addit} => {work} 0.1333333 1.0000000 2.500000 4
## [8759] {dataset,
## addit,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [8760] {network,
## addit,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [8761] {network,
## dataset,
## addit} => {work} 0.1333333 1.0000000 2.500000 4
## [8762] {propos,
## addit,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [8763] {network,
## addit,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [8764] {network,
## propos,
## addit} => {work} 0.1333333 1.0000000 2.500000 4
## [8765] {network,
## propos,
## work} => {addit} 0.1333333 0.8000000 4.800000 4
## [8766] {dataset,
## propos,
## addit} => {network} 0.1333333 1.0000000 1.578947 4
## [8767] {network,
## dataset,
## addit} => {propos} 0.1333333 1.0000000 2.000000 4
## [8768] {network,
## propos,
## addit} => {dataset} 0.1333333 1.0000000 2.307692 4
## [8769] {machin,
## recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8770] {problem,
## recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [8771] {machin,
## problem,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [8772] {machin,
## problem,
## recent} => {function} 0.1000000 1.0000000 6.000000 3
## [8773] {machin,
## recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8774] {show,
## recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [8775] {machin,
## show,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [8776] {machin,
## show,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [8777] {machin,
## recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8778] {model,
## recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [8779] {machin,
## model,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [8780] {machin,
## model,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [8781] {problem,
## recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8782] {show,
## recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8783] {show,
## problem,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [8784] {show,
## problem,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [8785] {problem,
## recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8786] {model,
## recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8787] {model,
## problem,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [8788] {model,
## problem,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [8789] {show,
## recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8790] {model,
## recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8791] {model,
## show,
## function} => {recent} 0.1000000 0.7500000 3.214286 3
## [8792] {machin,
## problem,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8793] {machin,
## show,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8794] {show,
## problem,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [8795] {machin,
## show,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [8796] {machin,
## problem,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8797] {machin,
## model,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8798] {model,
## problem,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [8799] {machin,
## model,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [8800] {machin,
## show,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8801] {machin,
## model,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8802] {model,
## show,
## function} => {machin} 0.1000000 0.7500000 3.214286 3
## [8803] {object,
## optim,
## function} => {problem} 0.1000000 0.7500000 2.500000 3
## [8804] {optim,
## problem,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8805] {object,
## problem,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8806] {object,
## optim,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [8807] {object,
## optim,
## function} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [8808] {algorithm,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8809] {algorithm,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8810] {algorithm,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8811] {object,
## optim,
## function} => {task} 0.1000000 0.7500000 2.045455 3
## [8812] {task,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8813] {task,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8814] {task,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8815] {object,
## optim,
## function} => {data} 0.1000000 0.7500000 1.730769 3
## [8816] {data,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8817] {data,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8818] {data,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8819] {object,
## optim,
## function} => {show} 0.1000000 0.7500000 1.406250 3
## [8820] {show,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8821] {show,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8822] {show,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8823] {object,
## optim,
## function} => {propos} 0.1000000 0.7500000 1.500000 3
## [8824] {propos,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8825] {object,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8826] {object,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8827] {object,
## optim,
## function} => {model} 0.1000000 0.7500000 1.406250 3
## [8828] {model,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8829] {model,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8830] {model,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8831] {object,
## optim,
## function} => {featur} 0.1000000 0.7500000 1.406250 3
## [8832] {featur,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8833] {featur,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8834] {featur,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8835] {optim,
## problem,
## function} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [8836] {algorithm,
## optim,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8837] {algorithm,
## problem,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8838] {algorithm,
## optim,
## problem} => {function} 0.1000000 0.7500000 4.500000 3
## [8839] {task,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8840] {data,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8841] {data,
## task,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8842] {data,
## task,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8843] {task,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8844] {propos,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8845] {task,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8846] {task,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8847] {task,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8848] {featur,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8849] {featur,
## task,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8850] {featur,
## task,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8851] {data,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8852] {propos,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8853] {data,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8854] {data,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8855] {data,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8856] {featur,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8857] {data,
## featur,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8858] {data,
## featur,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [8859] {show,
## optim,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8860] {model,
## optim,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8861] {model,
## show,
## function} => {optim} 0.1000000 0.7500000 3.214286 3
## [8862] {model,
## show,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8863] {propos,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8864] {featur,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8865] {featur,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8866] {featur,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8867] {object,
## problem,
## function} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [8868] {algorithm,
## object,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8869] {algorithm,
## problem,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8870] {algorithm,
## object,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [8871] {task,
## object,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8872] {data,
## object,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8873] {data,
## task,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8874] {data,
## task,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [8875] {task,
## object,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8876] {object,
## propos,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8877] {task,
## propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8878] {task,
## object,
## propos} => {function} 0.1000000 0.7500000 4.500000 3
## [8879] {task,
## object,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8880] {featur,
## object,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8881] {featur,
## task,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8882] {featur,
## task,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [8883] {data,
## object,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8884] {object,
## propos,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8885] {data,
## propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8886] {data,
## object,
## propos} => {function} 0.1000000 0.7500000 4.500000 3
## [8887] {data,
## object,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8888] {featur,
## object,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8889] {data,
## featur,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8890] {data,
## featur,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [8891] {show,
## object,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8892] {model,
## object,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8893] {model,
## show,
## function} => {object} 0.1000000 0.7500000 2.812500 3
## [8894] {model,
## show,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [8895] {object,
## propos,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8896] {featur,
## object,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8897] {featur,
## propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8898] {perform,
## problem,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8899] {featur,
## problem,
## function} => {perform} 0.1000000 1.0000000 2.142857 3
## [8900] {featur,
## perform,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8901] {show,
## problem,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8902] {model,
## problem,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8903] {model,
## show,
## function} => {problem} 0.1000000 0.7500000 2.500000 3
## [8904] {model,
## show,
## problem} => {function} 0.1000000 0.7500000 4.500000 3
## [8905] {method,
## show,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8906] {method,
## model,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8907] {model,
## show,
## function} => {method} 0.1000000 0.7500000 2.045455 3
## [8908] {data,
## task,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8909] {task,
## propos,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8910] {data,
## propos,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8911] {data,
## task,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8912] {featur,
## task,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8913] {data,
## featur,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8914] {task,
## propos,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8915] {featur,
## task,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8916] {featur,
## propos,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8917] {data,
## propos,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8918] {data,
## featur,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8919] {featur,
## propos,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8920] {show,
## learn,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8921] {model,
## learn,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8922] {model,
## show,
## function} => {learn} 0.1000000 0.7500000 1.730769 3
## [8923] {show,
## learn,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8924] {featur,
## learn,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8925] {featur,
## show,
## function} => {learn} 0.1000000 1.0000000 2.307692 3
## [8926] {model,
## learn,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8927] {featur,
## learn,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8928] {featur,
## model,
## function} => {learn} 0.1000000 1.0000000 2.307692 3
## [8929] {model,
## show,
## function} => {featur} 0.1000000 0.7500000 1.406250 3
## [8930] {featur,
## show,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8931] {featur,
## model,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8932] {accuraci,
## learn,
## effect} => {represent} 0.1000000 1.0000000 2.000000 3
## [8933] {represent,
## accuraci,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [8934] {represent,
## accuraci,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [8935] {represent,
## learn,
## effect} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [8936] {accuraci,
## learn,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [8937] {featur,
## accuraci,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [8938] {featur,
## accuraci,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [8939] {featur,
## learn,
## effect} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [8940] {represent,
## accuraci,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [8941] {featur,
## accuraci,
## effect} => {represent} 0.1000000 1.0000000 2.000000 3
## [8942] {featur,
## represent,
## accuraci} => {effect} 0.1000000 0.7500000 3.214286 3
## [8943] {featur,
## represent,
## effect} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8944] {paper,
## accuraci,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [8945] {train,
## accuraci,
## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [8946] {paper,
## train,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8947] {paper,
## train,
## achiev} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8948] {paper,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [8949] {accuraci,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [8950] {paper,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8951] {paper,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8952] {paper,
## accuraci,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [8953] {represent,
## accuraci,
## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [8954] {paper,
## represent,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8955] {paper,
## represent,
## achiev} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8956] {paper,
## accuraci,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [8957] {featur,
## accuraci,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [8958] {featur,
## paper,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8959] {featur,
## paper,
## achiev} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8960] {accuraci,
## achiev,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8961] {accuraci,
## achiev,
## dataset} => {recognit} 0.1000000 1.0000000 3.333333 3
## [8962] {accuraci,
## dataset,
## recognit} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8963] {achiev,
## dataset,
## recognit} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8964] {accuraci,
## achiev,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [8965] {featur,
## accuraci,
## achiev} => {recognit} 0.1000000 0.7500000 2.500000 3
## [8966] {featur,
## accuraci,
## recognit} => {achiev} 0.1000000 0.7500000 3.214286 3
## [8967] {featur,
## achiev,
## recognit} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8968] {accuraci,
## achiev,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [8969] {featur,
## accuraci,
## achiev} => {result} 0.1000000 0.7500000 2.250000 3
## [8970] {featur,
## accuraci,
## result} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8971] {featur,
## achiev,
## result} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8972] {accuraci,
## achiev,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [8973] {network,
## accuraci,
## achiev} => {result} 0.1000000 1.0000000 3.000000 3
## [8974] {network,
## accuraci,
## result} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8975] {network,
## achiev,
## result} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [8976] {accuraci,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [8977] {accuraci,
## achiev,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [8978] {accuraci,
## neural,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8979] {achiev,
## neural,
## propos} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [8980] {accuraci,
## achiev,
## neural} => {featur} 0.1000000 1.0000000 1.875000 3
## [8981] {featur,
## accuraci,
## achiev} => {neural} 0.1000000 0.7500000 2.250000 3
## [8982] {featur,
## accuraci,
## neural} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8983] {featur,
## achiev,
## neural} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8984] {train,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [8985] {accuraci,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [8986] {train,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8987] {train,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8988] {train,
## accuraci,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [8989] {represent,
## accuraci,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [8990] {represent,
## train,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8991] {represent,
## train,
## achiev} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8992] {train,
## accuraci,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [8993] {featur,
## accuraci,
## achiev} => {train} 0.1000000 0.7500000 1.875000 3
## [8994] {featur,
## train,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8995] {featur,
## train,
## achiev} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [8996] {accuraci,
## achiev,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [8997] {featur,
## accuraci,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8998] {featur,
## accuraci,
## dataset} => {achiev} 0.1000000 1.0000000 4.285714 3
## [8999] {featur,
## achiev,
## dataset} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9000] {accuraci,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [9001] {represent,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [9002] {represent,
## accuraci,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [9003] {represent,
## achiev,
## learn} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9004] {accuraci,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [9005] {featur,
## accuraci,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [9006] {featur,
## accuraci,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [9007] {featur,
## achiev,
## learn} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9008] {represent,
## accuraci,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [9009] {featur,
## accuraci,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [9010] {featur,
## represent,
## accuraci} => {achiev} 0.1000000 0.7500000 3.214286 3
## [9011] {featur,
## represent,
## achiev} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9012] {accuraci,
## achiev,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [9013] {featur,
## accuraci,
## achiev} => {propos} 0.1000000 0.7500000 1.500000 3
## [9014] {featur,
## accuraci,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [9015] {featur,
## achiev,
## propos} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9016] {featur,
## accuraci,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [9017] {network,
## accuraci,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [9018] {featur,
## network,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9019] {featur,
## network,
## achiev} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9020] {classif,
## method,
## accuraci} => {propos} 0.1000000 1.0000000 2.000000 3
## [9021] {classif,
## accuraci,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [9022] {method,
## accuraci,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [9023] {classif,
## method,
## propos} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9024] {classif,
## method,
## accuraci} => {featur} 0.1000000 1.0000000 1.875000 3
## [9025] {classif,
## featur,
## accuraci} => {method} 0.1000000 1.0000000 2.727273 3
## [9026] {featur,
## method,
## accuraci} => {classif} 0.1000000 1.0000000 3.750000 3
## [9027] {classif,
## accuraci,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [9028] {classif,
## featur,
## accuraci} => {propos} 0.1000000 1.0000000 2.000000 3
## [9029] {featur,
## accuraci,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [9030] {paper,
## train,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [9031] {paper,
## accuraci,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [9032] {train,
## accuraci,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [9033] {paper,
## train,
## learn} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9034] {paper,
## train,
## accuraci} => {represent} 0.1000000 1.0000000 2.000000 3
## [9035] {paper,
## represent,
## accuraci} => {train} 0.1000000 1.0000000 2.500000 3
## [9036] {represent,
## train,
## accuraci} => {paper} 0.1000000 1.0000000 3.000000 3
## [9037] {paper,
## represent,
## train} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9038] {paper,
## train,
## accuraci} => {featur} 0.1000000 1.0000000 1.875000 3
## [9039] {featur,
## paper,
## accuraci} => {train} 0.1000000 1.0000000 2.500000 3
## [9040] {featur,
## train,
## accuraci} => {paper} 0.1000000 1.0000000 3.000000 3
## [9041] {paper,
## accuraci,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [9042] {paper,
## represent,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [9043] {represent,
## accuraci,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [9044] {paper,
## represent,
## learn} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9045] {paper,
## accuraci,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [9046] {featur,
## paper,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [9047] {featur,
## accuraci,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [9048] {featur,
## paper,
## learn} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9049] {paper,
## represent,
## accuraci} => {featur} 0.1000000 1.0000000 1.875000 3
## [9050] {featur,
## paper,
## accuraci} => {represent} 0.1000000 1.0000000 2.000000 3
## [9051] {featur,
## represent,
## accuraci} => {paper} 0.1000000 0.7500000 2.250000 3
## [9052] {featur,
## paper,
## represent} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9053] {accuraci,
## perform,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [9054] {accuraci,
## propos,
## recognit} => {perform} 0.1000000 1.0000000 2.142857 3
## [9055] {accuraci,
## perform,
## propos} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9056] {perform,
## propos,
## recognit} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9057] {accuraci,
## perform,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [9058] {featur,
## accuraci,
## recognit} => {perform} 0.1000000 0.7500000 1.607143 3
## [9059] {featur,
## accuraci,
## perform} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9060] {featur,
## perform,
## recognit} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [9061] {accuraci,
## dataset,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [9062] {featur,
## accuraci,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9063] {featur,
## accuraci,
## dataset} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9064] {featur,
## dataset,
## recognit} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9065] {accuraci,
## recognit,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [9066] {represent,
## accuraci,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [9067] {represent,
## accuraci,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [9068] {accuraci,
## recognit,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [9069] {featur,
## accuraci,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [9070] {featur,
## accuraci,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [9071] {represent,
## accuraci,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [9072] {featur,
## accuraci,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [9073] {featur,
## represent,
## accuraci} => {recognit} 0.1000000 0.7500000 2.500000 3
## [9074] {featur,
## represent,
## recognit} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9075] {show,
## accuraci,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [9076] {featur,
## accuraci,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [9077] {featur,
## show,
## accuraci} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9078] {featur,
## show,
## recognit} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9079] {accuraci,
## propos,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [9080] {featur,
## accuraci,
## recognit} => {propos} 0.1000000 0.7500000 1.500000 3
## [9081] {featur,
## accuraci,
## propos} => {recognit} 0.1000000 0.7500000 2.500000 3
## [9082] {featur,
## propos,
## recognit} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9083] {featur,
## accuraci,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [9084] {network,
## accuraci,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [9085] {featur,
## network,
## accuraci} => {result} 0.1000000 1.0000000 3.000000 3
## [9086] {featur,
## network,
## result} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9087] {accuraci,
## neural,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [9088] {featur,
## accuraci,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [9089] {featur,
## accuraci,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [9090] {featur,
## neural,
## propos} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [9091] {method,
## accuraci,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [9092] {featur,
## method,
## accuraci} => {propos} 0.1000000 1.0000000 2.000000 3
## [9093] {featur,
## accuraci,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [9094] {train,
## accuraci,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [9095] {represent,
## train,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [9096] {represent,
## accuraci,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [9097] {represent,
## train,
## learn} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9098] {train,
## accuraci,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [9099] {featur,
## train,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [9100] {featur,
## accuraci,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [9101] {featur,
## train,
## learn} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [9102] {represent,
## train,
## accuraci} => {featur} 0.1000000 1.0000000 1.875000 3
## [9103] {featur,
## train,
## accuraci} => {represent} 0.1000000 1.0000000 2.000000 3
## [9104] {featur,
## represent,
## accuraci} => {train} 0.1000000 0.7500000 1.875000 3
## [9105] {accuraci,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [9106] {featur,
## accuraci,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9107] {featur,
## accuraci,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [9108] {represent,
## accuraci,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [9109] {accuraci,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [9110] {represent,
## accuraci,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [9111] {represent,
## accuraci,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [9112] {featur,
## accuraci,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [9113] {featur,
## represent,
## accuraci} => {learn} 0.1333333 1.0000000 2.307692 4
## [9114] {accuraci,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [9115] {featur,
## accuraci,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [9116] {featur,
## accuraci,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [9117] {represent,
## accuraci,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [9118] {featur,
## represent,
## accuraci} => {propos} 0.1000000 0.7500000 1.500000 3
## [9119] {featur,
## accuraci,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [9120] {train,
## neural,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [9121] {approach,
## neural,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [9122] {approach,
## train,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [9123] {approach,
## train,
## neural} => {base} 0.1000000 0.7500000 4.500000 3
## [9124] {train,
## neural,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [9125] {neural,
## propos,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [9126] {train,
## propos,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [9127] {train,
## neural,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [9128] {network,
## neural,
## base} => {train} 0.1000000 0.7500000 1.875000 3
## [9129] {network,
## train,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [9130] {approach,
## neural,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [9131] {neural,
## propos,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [9132] {approach,
## propos,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [9133] {approach,
## neural,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [9134] {network,
## neural,
## base} => {approach} 0.1000000 0.7500000 1.875000 3
## [9135] {approach,
## network,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [9136] {neural,
## propos,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [9137] {network,
## neural,
## base} => {propos} 0.1000000 0.7500000 1.500000 3
## [9138] {network,
## propos,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [9139] {approach,
## train,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [9140] {train,
## propos,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [9141] {approach,
## propos,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [9142] {approach,
## train,
## propos} => {base} 0.1000000 0.7500000 4.500000 3
## [9143] {approach,
## train,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [9144] {network,
## train,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [9145] {approach,
## network,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [9146] {train,
## propos,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [9147] {network,
## train,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [9148] {network,
## propos,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [9149] {network,
## train,
## propos} => {base} 0.1000000 0.7500000 4.500000 3
## [9150] {approach,
## propos,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [9151] {approach,
## network,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [9152] {network,
## propos,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [9153] {result,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [9154] {neural,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9155] {neural,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [9156] {neural,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [9157] {result,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [9158] {train,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9159] {train,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [9160] {train,
## result,
## specif} => {potenti} 0.1000000 0.7500000 4.500000 3
## [9161] {result,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9162] {dataset,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9163] {dataset,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [9164] {dataset,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [9165] {result,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9166] {network,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9167] {network,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [9168] {network,
## result,
## specif} => {potenti} 0.1000000 0.7500000 4.500000 3
## [9169] {neural,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [9170] {train,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [9171] {train,
## neural,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [9172] {train,
## neural,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [9173] {neural,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9174] {dataset,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [9175] {dataset,
## neural,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [9176] {dataset,
## neural,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [9177] {neural,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9178] {network,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [9179] {network,
## neural,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [9180] {network,
## neural,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [9181] {train,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9182] {dataset,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [9183] {train,
## dataset,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [9184] {train,
## dataset,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [9185] {train,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9186] {network,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [9187] {network,
## train,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [9188] {network,
## train,
## specif} => {potenti} 0.1000000 0.7500000 4.500000 3
## [9189] {dataset,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9190] {network,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9191] {network,
## dataset,
## potenti} => {specif} 0.1000000 0.7500000 4.500000 3
## [9192] {network,
## dataset,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [9193] {layer,
## applic,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9194] {result,
## layer,
## potenti} => {applic} 0.1000000 1.0000000 4.285714 3
## [9195] {result,
## applic,
## potenti} => {layer} 0.1000000 1.0000000 5.000000 3
## [9196] {result,
## layer,
## applic} => {potenti} 0.1000000 1.0000000 6.000000 3
## [9197] {layer,
## applic,
## potenti} => {data} 0.1000000 1.0000000 2.307692 3
## [9198] {data,
## layer,
## potenti} => {applic} 0.1000000 1.0000000 4.285714 3
## [9199] {data,
## applic,
## potenti} => {layer} 0.1000000 1.0000000 5.000000 3
## [9200] {data,
## layer,
## applic} => {potenti} 0.1000000 1.0000000 6.000000 3
## [9201] {result,
## layer,
## potenti} => {data} 0.1000000 1.0000000 2.307692 3
## [9202] {data,
## layer,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9203] {data,
## result,
## potenti} => {layer} 0.1000000 1.0000000 5.000000 3
## [9204] {data,
## result,
## layer} => {potenti} 0.1000000 0.7500000 4.500000 3
## [9205] {result,
## applic,
## potenti} => {data} 0.1000000 1.0000000 2.307692 3
## [9206] {data,
## applic,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9207] {data,
## result,
## potenti} => {applic} 0.1000000 1.0000000 4.285714 3
## [9208] {data,
## result,
## applic} => {potenti} 0.1000000 0.7500000 4.500000 3
## [9209] {neural,
## result,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [9210] {train,
## result,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [9211] {train,
## neural,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9212] {train,
## neural,
## result} => {potenti} 0.1000000 0.7500000 4.500000 3
## [9213] {neural,
## result,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9214] {dataset,
## result,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [9215] {dataset,
## neural,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9216] {dataset,
## neural,
## result} => {potenti} 0.1000000 0.7500000 4.500000 3
## [9217] {neural,
## result,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9218] {network,
## result,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [9219] {network,
## neural,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9220] {train,
## result,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9221] {dataset,
## result,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [9222] {train,
## dataset,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9223] {train,
## dataset,
## result} => {potenti} 0.1000000 0.7500000 4.500000 3
## [9224] {train,
## result,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9225] {network,
## result,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [9226] {network,
## train,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [9227] {dataset,
## result,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9228] {network,
## result,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9229] {network,
## dataset,
## potenti} => {result} 0.1000000 0.7500000 2.250000 3
## [9230] {train,
## neural,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9231] {dataset,
## neural,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [9232] {train,
## dataset,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [9233] {train,
## neural,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9234] {network,
## neural,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [9235] {network,
## train,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [9236] {dataset,
## neural,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9237] {network,
## neural,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9238] {network,
## dataset,
## potenti} => {neural} 0.1000000 0.7500000 2.250000 3
## [9239] {train,
## dataset,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9240] {network,
## train,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9241] {network,
## dataset,
## potenti} => {train} 0.1000000 0.7500000 1.875000 3
## [9242] {data,
## work,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9243] {dataset,
## work,
## potenti} => {data} 0.1000000 1.0000000 2.307692 3
## [9244] {data,
## dataset,
## potenti} => {work} 0.1000000 1.0000000 2.500000 3
## [9245] {data,
## work,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9246] {network,
## work,
## potenti} => {data} 0.1000000 1.0000000 2.307692 3
## [9247] {data,
## network,
## potenti} => {work} 0.1000000 1.0000000 2.500000 3
## [9248] {data,
## network,
## work} => {potenti} 0.1000000 0.7500000 4.500000 3
## [9249] {dataset,
## work,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9250] {network,
## work,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9251] {network,
## dataset,
## potenti} => {work} 0.1000000 0.7500000 1.875000 3
## [9252] {data,
## dataset,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9253] {data,
## network,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9254] {network,
## dataset,
## potenti} => {data} 0.1000000 0.7500000 1.730769 3
## [9255] {dataset,
## propos,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [9256] {network,
## dataset,
## potenti} => {propos} 0.1000000 0.7500000 1.500000 3
## [9257] {network,
## propos,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9258] {problem,
## solv,
## requir} => {model} 0.1000000 1.0000000 1.875000 3
## [9259] {model,
## solv,
## requir} => {problem} 0.1000000 1.0000000 3.333333 3
## [9260] {model,
## problem,
## solv} => {requir} 0.1000000 1.0000000 5.000000 3
## [9261] {model,
## problem,
## requir} => {solv} 0.1000000 1.0000000 5.000000 3
## [9262] {make,
## problem,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [9263] {approach,
## make,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [9264] {approach,
## problem,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [9265] {approach,
## make,
## problem} => {solv} 0.1000000 0.7500000 3.750000 3
## [9266] {make,
## problem,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [9267] {make,
## perform,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [9268] {perform,
## problem,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [9269] {make,
## problem,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [9270] {featur,
## make,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [9271] {featur,
## problem,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [9272] {featur,
## make,
## problem} => {solv} 0.1000000 1.0000000 5.000000 3
## [9273] {approach,
## make,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [9274] {make,
## perform,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [9275] {approach,
## perform,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [9276] {approach,
## make,
## perform} => {solv} 0.1000000 0.7500000 3.750000 3
## [9277] {approach,
## make,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [9278] {featur,
## make,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [9279] {approach,
## featur,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [9280] {make,
## perform,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [9281] {featur,
## make,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [9282] {featur,
## perform,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [9283] {featur,
## make,
## perform} => {solv} 0.1000000 0.7500000 3.750000 3
## [9284] {approach,
## problem,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [9285] {perform,
## problem,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [9286] {approach,
## perform,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [9287] {approach,
## problem,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [9288] {featur,
## problem,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [9289] {approach,
## featur,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [9290] {approach,
## featur,
## problem} => {solv} 0.1000000 1.0000000 5.000000 3
## [9291] {perform,
## problem,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [9292] {featur,
## problem,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [9293] {featur,
## perform,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [9294] {data,
## task,
## solv} => {represent} 0.1000000 0.7500000 1.500000 3
## [9295] {represent,
## task,
## solv} => {data} 0.1000000 1.0000000 2.307692 3
## [9296] {data,
## represent,
## solv} => {task} 0.1000000 1.0000000 2.727273 3
## [9297] {data,
## task,
## solv} => {featur} 0.1000000 0.7500000 1.406250 3
## [9298] {featur,
## task,
## solv} => {data} 0.1000000 1.0000000 2.307692 3
## [9299] {data,
## featur,
## solv} => {task} 0.1000000 1.0000000 2.727273 3
## [9300] {represent,
## task,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [9301] {featur,
## task,
## solv} => {represent} 0.1000000 1.0000000 2.000000 3
## [9302] {featur,
## represent,
## solv} => {task} 0.1000000 1.0000000 2.727273 3
## [9303] {approach,
## perform,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [9304] {approach,
## featur,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [9305] {featur,
## perform,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [9306] {approach,
## featur,
## perform} => {solv} 0.1000000 0.7500000 3.750000 3
## [9307] {data,
## represent,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [9308] {data,
## featur,
## solv} => {represent} 0.1000000 1.0000000 2.000000 3
## [9309] {featur,
## represent,
## solv} => {data} 0.1000000 1.0000000 2.307692 3
## [9310] {dataset,
## learn,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [9311] {featur,
## dataset,
## solv} => {learn} 0.1000000 1.0000000 2.307692 3
## [9312] {featur,
## learn,
## solv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9313] {result,
## layer,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9314] {train,
## layer,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9315] {train,
## result,
## specif} => {layer} 0.1000000 0.7500000 3.750000 3
## [9316] {train,
## result,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [9317] {result,
## layer,
## specif} => {work} 0.1000000 1.0000000 2.500000 3
## [9318] {work,
## layer,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9319] {result,
## work,
## specif} => {layer} 0.1000000 1.0000000 5.000000 3
## [9320] {result,
## work,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [9321] {result,
## layer,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9322] {network,
## layer,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9323] {network,
## result,
## specif} => {layer} 0.1000000 0.7500000 3.750000 3
## [9324] {network,
## result,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [9325] {train,
## layer,
## specif} => {work} 0.1000000 1.0000000 2.500000 3
## [9326] {work,
## layer,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9327] {train,
## work,
## specif} => {layer} 0.1000000 1.0000000 5.000000 3
## [9328] {train,
## work,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [9329] {train,
## layer,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9330] {network,
## layer,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9331] {network,
## train,
## specif} => {layer} 0.1000000 0.7500000 3.750000 3
## [9332] {network,
## train,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [9333] {work,
## layer,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9334] {network,
## layer,
## specif} => {work} 0.1000000 1.0000000 2.500000 3
## [9335] {network,
## work,
## specif} => {layer} 0.1000000 1.0000000 5.000000 3
## [9336] {network,
## work,
## layer} => {specif} 0.1000000 0.7500000 4.500000 3
## [9337] {neural,
## result,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9338] {train,
## result,
## specif} => {neural} 0.1000000 0.7500000 2.250000 3
## [9339] {train,
## neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9340] {train,
## neural,
## result} => {specif} 0.1000000 0.7500000 4.500000 3
## [9341] {neural,
## result,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9342] {dataset,
## result,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [9343] {dataset,
## neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9344] {dataset,
## neural,
## result} => {specif} 0.1000000 0.7500000 4.500000 3
## [9345] {neural,
## result,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9346] {network,
## result,
## specif} => {neural} 0.1000000 0.7500000 2.250000 3
## [9347] {network,
## neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9348] {algorithm,
## result,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9349] {train,
## result,
## specif} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [9350] {train,
## algorithm,
## specif} => {result} 0.1000000 0.7500000 2.250000 3
## [9351] {train,
## algorithm,
## result} => {specif} 0.1000000 1.0000000 6.000000 3
## [9352] {algorithm,
## result,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9353] {network,
## result,
## specif} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [9354] {network,
## algorithm,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9355] {network,
## algorithm,
## result} => {specif} 0.1000000 0.7500000 4.500000 3
## [9356] {train,
## result,
## specif} => {work} 0.1000000 0.7500000 1.875000 3
## [9357] {result,
## work,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9358] {train,
## work,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9359] {train,
## result,
## work} => {specif} 0.1000000 1.0000000 6.000000 3
## [9360] {train,
## result,
## specif} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9361] {dataset,
## result,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9362] {train,
## dataset,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9363] {train,
## dataset,
## result} => {specif} 0.1000000 0.7500000 4.500000 3
## [9364] {train,
## result,
## specif} => {network} 0.1333333 1.0000000 1.578947 4
## [9365] {network,
## result,
## specif} => {train} 0.1333333 1.0000000 2.500000 4
## [9366] {network,
## train,
## specif} => {result} 0.1333333 1.0000000 3.000000 4
## [9367] {result,
## work,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9368] {network,
## result,
## specif} => {work} 0.1000000 0.7500000 1.875000 3
## [9369] {network,
## work,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9370] {network,
## result,
## work} => {specif} 0.1000000 1.0000000 6.000000 3
## [9371] {dataset,
## result,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9372] {network,
## result,
## specif} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9373] {network,
## dataset,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9374] {train,
## neural,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9375] {dataset,
## neural,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9376] {train,
## dataset,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [9377] {train,
## neural,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9378] {network,
## neural,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9379] {network,
## train,
## specif} => {neural} 0.1000000 0.7500000 2.250000 3
## [9380] {dataset,
## neural,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9381] {network,
## neural,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9382] {network,
## dataset,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [9383] {train,
## algorithm,
## specif} => {network} 0.1000000 0.7500000 1.184211 3
## [9384] {network,
## algorithm,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9385] {network,
## train,
## specif} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [9386] {network,
## train,
## algorithm} => {specif} 0.1000000 0.7500000 4.500000 3
## [9387] {train,
## work,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9388] {network,
## train,
## specif} => {work} 0.1000000 0.7500000 1.875000 3
## [9389] {network,
## work,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9390] {network,
## train,
## work} => {specif} 0.1000000 0.7500000 4.500000 3
## [9391] {train,
## dataset,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9392] {network,
## train,
## specif} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9393] {network,
## dataset,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9394] {result,
## layer,
## techniqu} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [9395] {algorithm,
## layer,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [9396] {algorithm,
## result,
## techniqu} => {layer} 0.1000000 1.0000000 5.000000 3
## [9397] {algorithm,
## result,
## layer} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [9398] {machin,
## learn,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [9399] {featur,
## machin,
## techniqu} => {learn} 0.1000000 1.0000000 2.307692 3
## [9400] {featur,
## learn,
## techniqu} => {machin} 0.1000000 1.0000000 4.285714 3
## [9401] {featur,
## machin,
## learn} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [9402] {architectur,
## result,
## techniqu} => {represent} 0.1000000 1.0000000 2.000000 3
## [9403] {represent,
## architectur,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [9404] {represent,
## result,
## techniqu} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9405] {represent,
## architectur,
## result} => {techniqu} 0.1000000 1.0000000 6.000000 3
## [9406] {architectur,
## result,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [9407] {featur,
## architectur,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [9408] {featur,
## result,
## techniqu} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9409] {featur,
## architectur,
## result} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [9410] {represent,
## architectur,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [9411] {featur,
## architectur,
## techniqu} => {represent} 0.1000000 1.0000000 2.000000 3
## [9412] {featur,
## represent,
## techniqu} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9413] {featur,
## represent,
## architectur} => {techniqu} 0.1000000 1.0000000 6.000000 3
## [9414] {train,
## result,
## techniqu} => {network} 0.1000000 1.0000000 1.578947 3
## [9415] {network,
## result,
## techniqu} => {train} 0.1000000 1.0000000 2.500000 3
## [9416] {network,
## train,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [9417] {represent,
## result,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [9418] {featur,
## result,
## techniqu} => {represent} 0.1000000 1.0000000 2.000000 3
## [9419] {featur,
## represent,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [9420] {represent,
## appli,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [9421] {appli,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [9422] {represent,
## appli,
## propos} => {applic} 0.1000000 1.0000000 4.285714 3
## [9423] {appli,
## object,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9424] {appli,
## object,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [9425] {appli,
## object,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [9426] {featur,
## appli,
## object} => {perform} 0.1000000 1.0000000 2.142857 3
## [9427] {featur,
## appli,
## perform} => {object} 0.1000000 0.7500000 2.812500 3
## [9428] {featur,
## object,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [9429] {appli,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [9430] {featur,
## appli,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [9431] {featur,
## appli,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [9432] {method,
## appli,
## architectur} => {perform} 0.1000000 1.0000000 2.142857 3
## [9433] {appli,
## architectur,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [9434] {method,
## appli,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [9435] {method,
## architectur,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [9436] {method,
## appli,
## architectur} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9437] {appli,
## architectur,
## dataset} => {method} 0.1000000 1.0000000 2.727273 3
## [9438] {method,
## appli,
## dataset} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9439] {method,
## architectur,
## dataset} => {appli} 0.1000000 1.0000000 5.000000 3
## [9440] {method,
## appli,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [9441] {appli,
## architectur,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [9442] {method,
## appli,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [9443] {method,
## architectur,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9444] {method,
## appli,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [9445] {network,
## appli,
## architectur} => {method} 0.1000000 1.0000000 2.727273 3
## [9446] {method,
## network,
## appli} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9447] {method,
## network,
## architectur} => {appli} 0.1000000 0.7500000 3.750000 3
## [9448] {appli,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9449] {appli,
## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [9450] {appli,
## dataset,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9451] {architectur,
## dataset,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [9452] {appli,
## architectur,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9453] {appli,
## architectur,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [9454] {architectur,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [9455] {appli,
## architectur,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [9456] {network,
## appli,
## architectur} => {perform} 0.1000000 1.0000000 2.142857 3
## [9457] {network,
## appli,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9458] {appli,
## architectur,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [9459] {appli,
## architectur,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9460] {appli,
## dataset,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [9461] {architectur,
## dataset,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9462] {appli,
## architectur,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [9463] {network,
## appli,
## architectur} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9464] {network,
## appli,
## dataset} => {architectur} 0.1000000 0.7500000 2.812500 3
## [9465] {appli,
## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [9466] {network,
## appli,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [9467] {network,
## appli,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [9468] {classif,
## method,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9469] {classif,
## appli,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [9470] {method,
## appli,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [9471] {classif,
## method,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [9472] {classif,
## method,
## appli} => {show} 0.1000000 1.0000000 1.875000 3
## [9473] {classif,
## show,
## appli} => {method} 0.1000000 1.0000000 2.727273 3
## [9474] {method,
## show,
## appli} => {classif} 0.1000000 1.0000000 3.750000 3
## [9475] {classif,
## method,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9476] {classif,
## appli,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [9477] {method,
## appli,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [9478] {classif,
## method,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9479] {classif,
## appli,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [9480] {classif,
## show,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9481] {show,
## appli,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [9482] {classif,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9483] {classif,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [9484] {classif,
## perform,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9485] {classif,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9486] {classif,
## appli,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [9487] {show,
## appli,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [9488] {classif,
## show,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9489] {appli,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [9490] {appli,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [9491] {show,
## appli,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [9492] {appli,
## propos,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [9493] {show,
## appli,
## propos} => {recognit} 0.1000000 0.7500000 2.500000 3
## [9494] {show,
## propos,
## recognit} => {appli} 0.1000000 0.7500000 3.750000 3
## [9495] {approach,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9496] {appli,
## dataset,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [9497] {approach,
## appli,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [9498] {approach,
## dataset,
## neural} => {appli} 0.1000000 0.7500000 3.750000 3
## [9499] {approach,
## appli,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [9500] {show,
## appli,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [9501] {approach,
## show,
## appli} => {neural} 0.1000000 1.0000000 3.000000 3
## [9502] {approach,
## show,
## neural} => {appli} 0.1000000 0.7500000 3.750000 3
## [9503] {approach,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [9504] {appli,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [9505] {approach,
## appli,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [9506] {approach,
## appli,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [9507] {network,
## appli,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [9508] {approach,
## network,
## appli} => {neural} 0.1000000 1.0000000 3.000000 3
## [9509] {appli,
## dataset,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [9510] {show,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9511] {show,
## appli,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [9512] {show,
## dataset,
## neural} => {appli} 0.1000000 1.0000000 5.000000 3
## [9513] {appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [9514] {appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9515] {appli,
## dataset,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [9516] {appli,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [9517] {network,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9518] {network,
## appli,
## dataset} => {neural} 0.1000000 0.7500000 2.250000 3
## [9519] {show,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [9520] {appli,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [9521] {show,
## appli,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [9522] {show,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [9523] {show,
## appli,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [9524] {network,
## appli,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [9525] {network,
## show,
## appli} => {neural} 0.1000000 1.0000000 3.000000 3
## [9526] {appli,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [9527] {network,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [9528] {network,
## appli,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [9529] {method,
## appli,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9530] {method,
## appli,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [9531] {appli,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [9532] {method,
## appli,
## perform} => {show} 0.1000000 0.7500000 1.406250 3
## [9533] {method,
## show,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9534] {show,
## appli,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [9535] {method,
## appli,
## perform} => {propos} 0.1333333 1.0000000 2.000000 4
## [9536] {method,
## appli,
## propos} => {perform} 0.1333333 1.0000000 2.142857 4
## [9537] {appli,
## perform,
## propos} => {method} 0.1333333 0.8000000 2.181818 4
## [9538] {method,
## perform,
## propos} => {appli} 0.1333333 0.8000000 4.000000 4
## [9539] {method,
## appli,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [9540] {featur,
## method,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9541] {featur,
## appli,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [9542] {featur,
## method,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [9543] {method,
## appli,
## perform} => {network} 0.1000000 0.7500000 1.184211 3
## [9544] {method,
## network,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9545] {network,
## appli,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [9546] {method,
## network,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [9547] {method,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [9548] {method,
## appli,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9549] {appli,
## dataset,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [9550] {method,
## appli,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [9551] {method,
## network,
## appli} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9552] {network,
## appli,
## dataset} => {method} 0.1000000 0.7500000 2.045455 3
## [9553] {method,
## network,
## dataset} => {appli} 0.1000000 0.7500000 3.750000 3
## [9554] {method,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9555] {method,
## appli,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [9556] {show,
## appli,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [9557] {method,
## appli,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [9558] {featur,
## method,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9559] {featur,
## appli,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [9560] {method,
## appli,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [9561] {method,
## network,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9562] {network,
## appli,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [9563] {approach,
## algorithm,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9564] {algorithm,
## appli,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [9565] {approach,
## appli,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [9566] {approach,
## algorithm,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [9567] {approach,
## algorithm,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9568] {algorithm,
## appli,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [9569] {approach,
## appli,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [9570] {approach,
## algorithm,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9571] {algorithm,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9572] {algorithm,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [9573] {algorithm,
## perform,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9574] {approach,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9575] {approach,
## appli,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [9576] {approach,
## appli,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [9577] {approach,
## show,
## appli} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9578] {show,
## appli,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [9579] {approach,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [9580] {approach,
## appli,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9581] {appli,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [9582] {approach,
## appli,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [9583] {approach,
## network,
## appli} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9584] {network,
## appli,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [9585] {approach,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9586] {approach,
## appli,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [9587] {show,
## appli,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [9588] {approach,
## show,
## appli} => {network} 0.1000000 1.0000000 1.578947 3
## [9589] {approach,
## network,
## appli} => {show} 0.1000000 1.0000000 1.875000 3
## [9590] {network,
## show,
## appli} => {approach} 0.1000000 1.0000000 2.500000 3
## [9591] {approach,
## appli,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [9592] {approach,
## network,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9593] {network,
## appli,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [9594] {appli,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [9595] {appli,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9596] {appli,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [9597] {appli,
## dataset,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [9598] {network,
## appli,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9599] {network,
## appli,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [9600] {appli,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [9601] {network,
## appli,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [9602] {network,
## appli,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [9603] {appli,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9604] {appli,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [9605] {appli,
## dataset,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [9606] {network,
## appli,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9607] {network,
## appli,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [9608] {network,
## dataset,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [9609] {show,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9610] {show,
## appli,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [9611] {appli,
## perform,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [9612] {featur,
## appli,
## perform} => {propos} 0.1333333 1.0000000 2.000000 4
## [9613] {featur,
## appli,
## propos} => {perform} 0.1333333 1.0000000 2.142857 4
## [9614] {network,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9615] {network,
## appli,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [9616] {network,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [9617] {show,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [9618] {appli,
## dataset,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [9619] {show,
## appli,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9620] {show,
## appli,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [9621] {network,
## appli,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [9622] {network,
## show,
## appli} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9623] {appli,
## dataset,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [9624] {network,
## appli,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [9625] {network,
## appli,
## propos} => {dataset} 0.1333333 1.0000000 2.307692 4
## [9626] {show,
## appli,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [9627] {network,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9628] {network,
## appli,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [9629] {network,
## show,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9630] {perform,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9631] {represent,
## work,
## larg} => {perform} 0.1000000 0.7500000 1.607143 3
## [9632] {represent,
## perform,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9633] {represent,
## perform,
## work} => {larg} 0.1000000 1.0000000 6.000000 3
## [9634] {data,
## work,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9635] {dataset,
## work,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [9636] {data,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9637] {data,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9638] {represent,
## work,
## larg} => {data} 0.1000000 0.7500000 1.730769 3
## [9639] {data,
## represent,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9640] {data,
## represent,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [9641] {data,
## work,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [9642] {featur,
## work,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [9643] {data,
## featur,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9644] {data,
## featur,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [9645] {dataset,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9646] {represent,
## work,
## larg} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9647] {represent,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9648] {represent,
## dataset,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [9649] {dataset,
## work,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [9650] {featur,
## work,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9651] {featur,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9652] {represent,
## work,
## larg} => {propos} 0.1000000 0.7500000 1.500000 3
## [9653] {propos,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9654] {represent,
## propos,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9655] {represent,
## propos,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [9656] {represent,
## work,
## larg} => {featur} 0.1000000 0.7500000 1.406250 3
## [9657] {featur,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9658] {featur,
## represent,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9659] {featur,
## represent,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [9660] {data,
## dataset,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9661] {data,
## represent,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9662] {represent,
## dataset,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [9663] {data,
## dataset,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [9664] {data,
## featur,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9665] {featur,
## dataset,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [9666] {data,
## represent,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [9667] {data,
## featur,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9668] {featur,
## represent,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [9669] {represent,
## dataset,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [9670] {featur,
## dataset,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9671] {featur,
## represent,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9672] {data,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9673] {achiev,
## dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9674] {data,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9675] {data,
## achiev,
## dataset} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9676] {data,
## achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9677] {achiev,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9678] {data,
## learn,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [9679] {data,
## achiev,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9680] {data,
## achiev,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9681] {represent,
## achiev,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9682] {data,
## represent,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9683] {data,
## represent,
## achiev} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9684] {data,
## achiev,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9685] {featur,
## achiev,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9686] {data,
## featur,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [9687] {data,
## featur,
## achiev} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9688] {achiev,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9689] {achiev,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9690] {dataset,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9691] {achiev,
## dataset,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9692] {achiev,
## dataset,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9693] {represent,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9694] {represent,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9695] {represent,
## achiev,
## dataset} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9696] {achiev,
## dataset,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9697] {featur,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9698] {featur,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9699] {featur,
## achiev,
## dataset} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9700] {achiev,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9701] {represent,
## achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9702] {represent,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9703] {represent,
## achiev,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9704] {achiev,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9705] {featur,
## achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9706] {featur,
## learn,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [9707] {featur,
## achiev,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9708] {represent,
## achiev,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9709] {featur,
## achiev,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9710] {featur,
## represent,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9711] {featur,
## represent,
## achiev} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9712] {train,
## signific,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [9713] {show,
## signific,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9714] {show,
## train,
## challeng} => {signific} 0.1000000 1.0000000 3.750000 3
## [9715] {show,
## train,
## signific} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9716] {show,
## object,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [9717] {object,
## propos,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [9718] {show,
## propos,
## challeng} => {object} 0.1000000 1.0000000 3.750000 3
## [9719] {paper,
## train,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9720] {data,
## paper,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9721] {data,
## train,
## challeng} => {paper} 0.1000000 1.0000000 3.000000 3
## [9722] {data,
## paper,
## train} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9723] {paper,
## train,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9724] {paper,
## learn,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9725] {train,
## learn,
## challeng} => {paper} 0.1000000 1.0000000 3.000000 3
## [9726] {paper,
## train,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9727] {paper,
## train,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9728] {featur,
## paper,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9729] {featur,
## train,
## challeng} => {paper} 0.1000000 1.0000000 3.000000 3
## [9730] {data,
## paper,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9731] {paper,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9732] {data,
## learn,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [9733] {data,
## paper,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9734] {data,
## paper,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9735] {featur,
## paper,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9736] {data,
## featur,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [9737] {data,
## featur,
## paper} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9738] {paper,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9739] {featur,
## paper,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9740] {featur,
## learn,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [9741] {featur,
## paper,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9742] {train,
## recognit,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9743] {represent,
## recognit,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9744] {represent,
## train,
## challeng} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9745] {represent,
## train,
## recognit} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9746] {data,
## task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9747] {task,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9748] {data,
## learn,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [9749] {data,
## task,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [9750] {show,
## task,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9751] {data,
## show,
## challeng} => {task} 0.1000000 1.0000000 2.727273 3
## [9752] {data,
## task,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9753] {featur,
## task,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9754] {data,
## featur,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [9755] {task,
## learn,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [9756] {show,
## task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9757] {show,
## learn,
## challeng} => {task} 0.1000000 1.0000000 2.727273 3
## [9758] {task,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9759] {featur,
## task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9760] {featur,
## learn,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [9761] {show,
## task,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9762] {featur,
## task,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [9763] {featur,
## show,
## challeng} => {task} 0.1000000 1.0000000 2.727273 3
## [9764] {train,
## perform,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [9765] {train,
## propos,
## challeng} => {perform} 0.1000000 1.0000000 2.142857 3
## [9766] {perform,
## propos,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9767] {train,
## perform,
## propos} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9768] {data,
## train,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9769] {train,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9770] {data,
## learn,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [9771] {data,
## train,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9772] {data,
## train,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9773] {featur,
## train,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9774] {data,
## featur,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [9775] {train,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9776] {featur,
## train,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9777] {featur,
## learn,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [9778] {featur,
## train,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9779] {data,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9780] {data,
## learn,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9781] {dataset,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9782] {data,
## dataset,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9783] {data,
## represent,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9784] {represent,
## dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9785] {data,
## dataset,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9786] {data,
## featur,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9787] {featur,
## dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9788] {data,
## learn,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [9789] {data,
## represent,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9790] {represent,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9791] {data,
## learn,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [9792] {data,
## show,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9793] {show,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9794] {data,
## learn,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [9795] {data,
## propos,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9796] {propos,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9797] {data,
## learn,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [9798] {data,
## model,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9799] {model,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9800] {data,
## learn,
## challeng} => {featur} 0.1333333 1.0000000 1.875000 4
## [9801] {data,
## featur,
## challeng} => {learn} 0.1333333 1.0000000 2.307692 4
## [9802] {featur,
## learn,
## challeng} => {data} 0.1333333 1.0000000 2.307692 4
## [9803] {data,
## represent,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9804] {data,
## featur,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [9805] {featur,
## represent,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9806] {data,
## show,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9807] {data,
## featur,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [9808] {featur,
## show,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9809] {data,
## propos,
## challeng} => {model} 0.1000000 1.0000000 1.875000 3
## [9810] {data,
## model,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [9811] {model,
## propos,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9812] {data,
## propos,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9813] {data,
## featur,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [9814] {featur,
## propos,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9815] {data,
## model,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9816] {data,
## featur,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [9817] {featur,
## model,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9818] {dataset,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9819] {represent,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9820] {represent,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9821] {dataset,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9822] {featur,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9823] {featur,
## learn,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9824] {represent,
## dataset,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9825] {featur,
## dataset,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9826] {featur,
## represent,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9827] {represent,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9828] {featur,
## learn,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [9829] {featur,
## represent,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9830] {show,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9831] {featur,
## learn,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [9832] {featur,
## show,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9833] {propos,
## learn,
## challeng} => {model} 0.1000000 1.0000000 1.875000 3
## [9834] {model,
## learn,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [9835] {model,
## propos,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9836] {propos,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9837] {featur,
## learn,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [9838] {featur,
## propos,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9839] {model,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9840] {featur,
## learn,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [9841] {featur,
## model,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9842] {model,
## propos,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9843] {featur,
## propos,
## challeng} => {model} 0.1000000 1.0000000 1.875000 3
## [9844] {featur,
## model,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [9845] {demonstr,
## imag,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [9846] {propos,
## demonstr,
## imag} => {work} 0.1000000 1.0000000 2.500000 3
## [9847] {propos,
## imag,
## work} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [9848] {propos,
## demonstr,
## work} => {imag} 0.1000000 0.7500000 4.500000 3
## [9849] {object,
## recognit,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [9850] {object,
## propos,
## imag} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9851] {propos,
## recognit,
## imag} => {object} 0.1000000 0.7500000 2.812500 3
## [9852] {object,
## propos,
## recognit} => {imag} 0.1000000 0.7500000 4.500000 3
## [9853] {perform,
## recognit,
## imag} => {represent} 0.1000000 1.0000000 2.000000 3
## [9854] {represent,
## recognit,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [9855] {represent,
## perform,
## imag} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9856] {represent,
## perform,
## recognit} => {imag} 0.1000000 1.0000000 6.000000 3
## [9857] {perform,
## recognit,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [9858] {propos,
## recognit,
## imag} => {perform} 0.1000000 0.7500000 1.607143 3
## [9859] {perform,
## propos,
## imag} => {recognit} 0.1000000 0.7500000 2.500000 3
## [9860] {perform,
## propos,
## recognit} => {imag} 0.1000000 0.7500000 4.500000 3
## [9861] {recognit,
## imag,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [9862] {propos,
## recognit,
## imag} => {learn} 0.1000000 0.7500000 1.730769 3
## [9863] {propos,
## imag,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9864] {propos,
## recognit,
## learn} => {imag} 0.1000000 0.7500000 4.500000 3
## [9865] {recognit,
## imag,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [9866] {featur,
## recognit,
## imag} => {learn} 0.1000000 1.0000000 2.307692 3
## [9867] {featur,
## imag,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9868] {represent,
## recognit,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [9869] {propos,
## recognit,
## imag} => {represent} 0.1000000 0.7500000 1.500000 3
## [9870] {represent,
## propos,
## imag} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9871] {represent,
## propos,
## recognit} => {imag} 0.1000000 0.7500000 4.500000 3
## [9872] {propos,
## recognit,
## imag} => {featur} 0.1000000 0.7500000 1.406250 3
## [9873] {featur,
## recognit,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [9874] {featur,
## propos,
## imag} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9875] {featur,
## propos,
## recognit} => {imag} 0.1000000 0.7500000 4.500000 3
## [9876] {train,
## improv,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [9877] {improv,
## perform,
## imag} => {train} 0.1000000 1.0000000 2.500000 3
## [9878] {train,
## perform,
## imag} => {improv} 0.1000000 1.0000000 3.333333 3
## [9879] {train,
## improv,
## perform} => {imag} 0.1000000 0.7500000 4.500000 3
## [9880] {train,
## improv,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [9881] {improv,
## propos,
## imag} => {train} 0.1000000 1.0000000 2.500000 3
## [9882] {train,
## propos,
## imag} => {improv} 0.1000000 1.0000000 3.333333 3
## [9883] {train,
## improv,
## propos} => {imag} 0.1000000 0.7500000 4.500000 3
## [9884] {improv,
## perform,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [9885] {improv,
## propos,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [9886] {perform,
## propos,
## imag} => {improv} 0.1000000 0.7500000 2.500000 3
## [9887] {improv,
## perform,
## propos} => {imag} 0.1000000 0.7500000 4.500000 3
## [9888] {train,
## perform,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [9889] {train,
## propos,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [9890] {perform,
## propos,
## imag} => {train} 0.1000000 0.7500000 1.875000 3
## [9891] {train,
## perform,
## propos} => {imag} 0.1000000 0.7500000 4.500000 3
## [9892] {represent,
## perform,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [9893] {perform,
## propos,
## imag} => {represent} 0.1000000 0.7500000 1.500000 3
## [9894] {represent,
## propos,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [9895] {show,
## perform,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [9896] {perform,
## propos,
## imag} => {show} 0.1000000 0.7500000 1.406250 3
## [9897] {show,
## propos,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [9898] {propos,
## imag,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [9899] {featur,
## imag,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [9900] {featur,
## propos,
## imag} => {learn} 0.1000000 1.0000000 2.307692 3
## [9901] {approach,
## dataset,
## requir} => {model} 0.1000000 1.0000000 1.875000 3
## [9902] {approach,
## model,
## requir} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9903] {model,
## dataset,
## requir} => {approach} 0.1000000 1.0000000 2.500000 3
## [9904] {reduc,
## exist,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [9905] {reduc,
## exist,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [9906] {exist,
## optim,
## work} => {reduc} 0.1000000 1.0000000 4.285714 3
## [9907] {reduc,
## optim,
## work} => {exist} 0.1000000 1.0000000 6.000000 3
## [9908] {reduc,
## exist,
## optim} => {network} 0.1000000 1.0000000 1.578947 3
## [9909] {network,
## reduc,
## exist} => {optim} 0.1000000 1.0000000 4.285714 3
## [9910] {network,
## exist,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [9911] {network,
## reduc,
## optim} => {exist} 0.1000000 1.0000000 6.000000 3
## [9912] {reduc,
## exist,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [9913] {network,
## reduc,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [9914] {network,
## exist,
## work} => {reduc} 0.1000000 0.7500000 3.214286 3
## [9915] {network,
## reduc,
## work} => {exist} 0.1000000 1.0000000 6.000000 3
## [9916] {exist,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [9917] {network,
## exist,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [9918] {network,
## exist,
## work} => {optim} 0.1000000 0.7500000 3.214286 3
## [9919] {network,
## optim,
## work} => {exist} 0.1000000 0.7500000 4.500000 3
## [9920] {approach,
## exist,
## work} => {show} 0.1000000 1.0000000 1.875000 3
## [9921] {approach,
## show,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [9922] {show,
## exist,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [9923] {approach,
## show,
## work} => {exist} 0.1000000 0.7500000 4.500000 3
## [9924] {approach,
## exist,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [9925] {approach,
## network,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [9926] {network,
## exist,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [9927] {approach,
## network,
## work} => {exist} 0.1000000 0.7500000 4.500000 3
## [9928] {approach,
## show,
## exist} => {network} 0.1000000 1.0000000 1.578947 3
## [9929] {approach,
## network,
## exist} => {show} 0.1000000 1.0000000 1.875000 3
## [9930] {network,
## show,
## exist} => {approach} 0.1000000 1.0000000 2.500000 3
## [9931] {show,
## exist,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [9932] {network,
## exist,
## work} => {show} 0.1000000 0.7500000 1.406250 3
## [9933] {network,
## show,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [9934] {network,
## show,
## work} => {exist} 0.1000000 0.7500000 4.500000 3
## [9935] {model,
## exist,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [9936] {network,
## exist,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [9937] {model,
## network,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [9938] {model,
## network,
## work} => {exist} 0.1000000 0.7500000 4.500000 3
## [9939] {represent,
## exist,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [9940] {model,
## exist,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [9941] {model,
## represent,
## exist} => {learn} 0.1000000 1.0000000 2.307692 3
## [9942] {represent,
## exist,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [9943] {featur,
## exist,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [9944] {featur,
## represent,
## exist} => {learn} 0.1000000 1.0000000 2.307692 3
## [9945] {model,
## exist,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [9946] {featur,
## exist,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [9947] {featur,
## model,
## exist} => {learn} 0.1000000 1.0000000 2.307692 3
## [9948] {model,
## represent,
## exist} => {featur} 0.1000000 1.0000000 1.875000 3
## [9949] {featur,
## represent,
## exist} => {model} 0.1000000 1.0000000 1.875000 3
## [9950] {featur,
## model,
## exist} => {represent} 0.1000000 1.0000000 2.000000 3
## [9951] {result,
## layer,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [9952] {data,
## layer,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [9953] {data,
## result,
## layer} => {applic} 0.1000000 0.7500000 3.214286 3
## [9954] {data,
## result,
## applic} => {layer} 0.1000000 0.7500000 3.750000 3
## [9955] {algorithm,
## process,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [9956] {represent,
## process,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [9957] {represent,
## algorithm,
## layer} => {process} 0.1000000 1.0000000 5.000000 3
## [9958] {represent,
## algorithm,
## process} => {layer} 0.1000000 1.0000000 5.000000 3
## [9959] {algorithm,
## process,
## layer} => {featur} 0.1000000 1.0000000 1.875000 3
## [9960] {featur,
## process,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [9961] {featur,
## algorithm,
## layer} => {process} 0.1000000 1.0000000 5.000000 3
## [9962] {featur,
## algorithm,
## process} => {layer} 0.1000000 0.7500000 3.750000 3
## [9963] {represent,
## process,
## layer} => {featur} 0.1000000 1.0000000 1.875000 3
## [9964] {featur,
## process,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [9965] {featur,
## represent,
## layer} => {process} 0.1000000 1.0000000 5.000000 3
## [9966] {featur,
## represent,
## process} => {layer} 0.1000000 1.0000000 5.000000 3
## [9967] {algorithm,
## result,
## layer} => {data} 0.1000000 0.7500000 1.730769 3
## [9968] {data,
## result,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [9969] {data,
## algorithm,
## layer} => {result} 0.1000000 1.0000000 3.000000 3
## [9970] {data,
## algorithm,
## result} => {layer} 0.1000000 1.0000000 5.000000 3
## [9971] {train,
## result,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [9972] {result,
## work,
## layer} => {train} 0.1000000 1.0000000 2.500000 3
## [9973] {train,
## work,
## layer} => {result} 0.1000000 1.0000000 3.000000 3
## [9974] {train,
## result,
## work} => {layer} 0.1000000 1.0000000 5.000000 3
## [9975] {train,
## result,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [9976] {network,
## result,
## layer} => {train} 0.1000000 1.0000000 2.500000 3
## [9977] {network,
## train,
## layer} => {result} 0.1000000 1.0000000 3.000000 3
## [9978] {result,
## work,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [9979] {network,
## result,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [9980] {network,
## work,
## layer} => {result} 0.1000000 0.7500000 2.250000 3
## [9981] {network,
## result,
## work} => {layer} 0.1000000 1.0000000 5.000000 3
## [9982] {data,
## result,
## layer} => {represent} 0.1000000 0.7500000 1.500000 3
## [9983] {represent,
## result,
## layer} => {data} 0.1000000 1.0000000 2.307692 3
## [9984] {data,
## represent,
## layer} => {result} 0.1000000 1.0000000 3.000000 3
## [9985] {data,
## result,
## layer} => {show} 0.1000000 0.7500000 1.406250 3
## [9986] {show,
## result,
## layer} => {data} 0.1000000 1.0000000 2.307692 3
## [9987] {data,
## show,
## layer} => {result} 0.1000000 1.0000000 3.000000 3
## [9988] {data,
## show,
## result} => {layer} 0.1000000 0.7500000 3.750000 3
## [9989] {neural,
## work,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [9990] {represent,
## neural,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [9991] {represent,
## work,
## layer} => {neural} 0.1000000 1.0000000 3.000000 3
## [9992] {represent,
## neural,
## work} => {layer} 0.1000000 1.0000000 5.000000 3
## [9993] {neural,
## work,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [9994] {network,
## neural,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [9995] {network,
## work,
## layer} => {neural} 0.1000000 0.7500000 2.250000 3
## [9996] {represent,
## neural,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [9997] {network,
## neural,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [9998] {network,
## represent,
## layer} => {neural} 0.1000000 1.0000000 3.000000 3
## [9999] {algorithm,
## work,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [10000] {network,
## algorithm,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [10001] {network,
## work,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [10002] {network,
## algorithm,
## work} => {layer} 0.1000000 0.7500000 3.750000 3
## [10003] {represent,
## algorithm,
## layer} => {featur} 0.1000000 1.0000000 1.875000 3
## [10004] {featur,
## algorithm,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [10005] {featur,
## represent,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [10006] {featur,
## represent,
## algorithm} => {layer} 0.1000000 0.7500000 3.750000 3
## [10007] {show,
## algorithm,
## layer} => {model} 0.1000000 1.0000000 1.875000 3
## [10008] {model,
## algorithm,
## layer} => {show} 0.1000000 1.0000000 1.875000 3
## [10009] {model,
## show,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [10010] {train,
## work,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [10011] {network,
## train,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [10012] {network,
## work,
## layer} => {train} 0.1000000 0.7500000 1.875000 3
## [10013] {network,
## train,
## work} => {layer} 0.1000000 0.7500000 3.750000 3
## [10014] {represent,
## work,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [10015] {network,
## work,
## layer} => {represent} 0.1000000 0.7500000 1.500000 3
## [10016] {network,
## represent,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [10017] {network,
## represent,
## work} => {layer} 0.1000000 0.7500000 3.750000 3
## [10018] {represent,
## layer,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [10019] {show,
## layer,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [10020] {represent,
## show,
## layer} => {learn} 0.1000000 1.0000000 2.307692 3
## [10021] {approach,
## complex,
## input} => {show} 0.1000000 1.0000000 1.875000 3
## [10022] {complex,
## input,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [10023] {approach,
## complex,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [10024] {approach,
## input,
## show} => {complex} 0.1000000 0.7500000 3.750000 3
## [10025] {approach,
## complex,
## input} => {model} 0.1000000 1.0000000 1.875000 3
## [10026] {complex,
## input,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [10027] {approach,
## complex,
## model} => {input} 0.1000000 1.0000000 4.285714 3
## [10028] {approach,
## input,
## model} => {complex} 0.1000000 0.7500000 3.750000 3
## [10029] {approach,
## complex,
## input} => {featur} 0.1000000 1.0000000 1.875000 3
## [10030] {complex,
## featur,
## input} => {approach} 0.1000000 1.0000000 2.500000 3
## [10031] {approach,
## complex,
## featur} => {input} 0.1000000 1.0000000 4.285714 3
## [10032] {approach,
## featur,
## input} => {complex} 0.1000000 0.7500000 3.750000 3
## [10033] {complex,
## input,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10034] {complex,
## input,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10035] {complex,
## model,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [10036] {complex,
## input,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10037] {complex,
## featur,
## input} => {show} 0.1000000 1.0000000 1.875000 3
## [10038] {complex,
## featur,
## show} => {input} 0.1000000 0.7500000 3.214286 3
## [10039] {featur,
## input,
## show} => {complex} 0.1000000 1.0000000 5.000000 3
## [10040] {complex,
## input,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [10041] {complex,
## featur,
## input} => {model} 0.1000000 1.0000000 1.875000 3
## [10042] {complex,
## featur,
## model} => {input} 0.1000000 1.0000000 4.285714 3
## [10043] {featur,
## input,
## model} => {complex} 0.1000000 1.0000000 5.000000 3
## [10044] {classif,
## complex,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [10045] {classif,
## complex,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [10046] {complex,
## method,
## show} => {classif} 0.1000000 1.0000000 3.750000 3
## [10047] {classif,
## complex,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [10048] {classif,
## complex,
## featur} => {method} 0.1000000 1.0000000 2.727273 3
## [10049] {complex,
## featur,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [10050] {classif,
## complex,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10051] {classif,
## complex,
## featur} => {show} 0.1000000 1.0000000 1.875000 3
## [10052] {complex,
## featur,
## show} => {classif} 0.1000000 0.7500000 2.812500 3
## [10053] {complex,
## method,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10054] {complex,
## featur,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [10055] {complex,
## featur,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [10056] {approach,
## complex,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10057] {approach,
## complex,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10058] {complex,
## model,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [10059] {approach,
## complex,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10060] {approach,
## complex,
## featur} => {show} 0.1000000 1.0000000 1.875000 3
## [10061] {complex,
## featur,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [10062] {approach,
## complex,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [10063] {approach,
## complex,
## featur} => {model} 0.1000000 1.0000000 1.875000 3
## [10064] {complex,
## featur,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [10065] {complex,
## show,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [10066] {complex,
## featur,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [10067] {complex,
## featur,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [10068] {complex,
## represent,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [10069] {complex,
## featur,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [10070] {complex,
## featur,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [10071] {complex,
## featur,
## represent} => {network} 0.1000000 0.7500000 1.184211 3
## [10072] {complex,
## network,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [10073] {complex,
## featur,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [10074] {complex,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10075] {complex,
## featur,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [10076] {complex,
## featur,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10077] {achiev,
## general,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10078] {achiev,
## dataset,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [10079] {dataset,
## general,
## result} => {achiev} 0.1000000 1.0000000 4.285714 3
## [10080] {achiev,
## dataset,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [10081] {achiev,
## general,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [10082] {network,
## achiev,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [10083] {network,
## general,
## result} => {achiev} 0.1000000 1.0000000 4.285714 3
## [10084] {network,
## achiev,
## result} => {general} 0.1000000 0.7500000 3.750000 3
## [10085] {achiev,
## dataset,
## general} => {network} 0.1000000 1.0000000 1.578947 3
## [10086] {network,
## achiev,
## general} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10087] {network,
## dataset,
## general} => {achiev} 0.1000000 1.0000000 4.285714 3
## [10088] {network,
## achiev,
## dataset} => {general} 0.1000000 0.7500000 3.750000 3
## [10089] {represent,
## general,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [10090] {show,
## general,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [10091] {represent,
## show,
## general} => {signific} 0.1000000 0.7500000 2.812500 3
## [10092] {represent,
## show,
## signific} => {general} 0.1000000 1.0000000 5.000000 3
## [10093] {general,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [10094] {show,
## general,
## recognit} => {result} 0.1000000 0.7500000 2.250000 3
## [10095] {show,
## general,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [10096] {show,
## recognit,
## result} => {general} 0.1000000 0.7500000 3.750000 3
## [10097] {general,
## recognit,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [10098] {featur,
## general,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [10099] {featur,
## general,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [10100] {featur,
## recognit,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [10101] {represent,
## general,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [10102] {show,
## general,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [10103] {represent,
## show,
## general} => {recognit} 0.1000000 0.7500000 2.500000 3
## [10104] {show,
## general,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [10105] {featur,
## general,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [10106] {featur,
## show,
## general} => {recognit} 0.1000000 1.0000000 3.333333 3
## [10107] {featur,
## show,
## recognit} => {general} 0.1000000 0.7500000 3.750000 3
## [10108] {algorithm,
## general,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [10109] {model,
## general,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [10110] {model,
## algorithm,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [10111] {model,
## algorithm,
## result} => {general} 0.1000000 0.7500000 3.750000 3
## [10112] {dataset,
## general,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [10113] {network,
## general,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10114] {network,
## dataset,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [10115] {show,
## general,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [10116] {featur,
## general,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [10117] {featur,
## show,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [10118] {featur,
## show,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [10119] {approach,
## dataset,
## general} => {propos} 0.1000000 1.0000000 2.000000 3
## [10120] {approach,
## general,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10121] {dataset,
## general,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [10122] {approach,
## dataset,
## general} => {model} 0.1000000 1.0000000 1.875000 3
## [10123] {approach,
## model,
## general} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10124] {model,
## dataset,
## general} => {approach} 0.1000000 1.0000000 2.500000 3
## [10125] {approach,
## general,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [10126] {approach,
## model,
## general} => {propos} 0.1000000 1.0000000 2.000000 3
## [10127] {model,
## general,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [10128] {show,
## general,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [10129] {general,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [10130] {show,
## general,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [10131] {dataset,
## general,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [10132] {model,
## dataset,
## general} => {propos} 0.1000000 1.0000000 2.000000 3
## [10133] {model,
## general,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10134] {represent,
## general,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [10135] {show,
## general,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [10136] {represent,
## show,
## general} => {learn} 0.1000000 0.7500000 1.730769 3
## [10137] {paper,
## signific,
## effect} => {task} 0.1000000 0.7500000 2.045455 3
## [10138] {task,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10139] {paper,
## task,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10140] {paper,
## task,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10141] {paper,
## signific,
## effect} => {train} 0.1000000 0.7500000 1.875000 3
## [10142] {train,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10143] {paper,
## train,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10144] {paper,
## train,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10145] {paper,
## signific,
## effect} => {learn} 0.1333333 1.0000000 2.307692 4
## [10146] {learn,
## signific,
## effect} => {paper} 0.1333333 1.0000000 3.000000 4
## [10147] {paper,
## learn,
## effect} => {signific} 0.1333333 1.0000000 3.750000 4
## [10148] {paper,
## learn,
## signific} => {effect} 0.1333333 1.0000000 4.285714 4
## [10149] {paper,
## signific,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [10150] {represent,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10151] {paper,
## represent,
## effect} => {signific} 0.1000000 0.7500000 2.812500 3
## [10152] {paper,
## represent,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10153] {paper,
## signific,
## effect} => {show} 0.1000000 0.7500000 1.406250 3
## [10154] {show,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10155] {paper,
## show,
## effect} => {signific} 0.1000000 0.7500000 2.812500 3
## [10156] {paper,
## show,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10157] {paper,
## signific,
## effect} => {propos} 0.1000000 0.7500000 1.500000 3
## [10158] {propos,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10159] {paper,
## propos,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10160] {paper,
## propos,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10161] {paper,
## signific,
## effect} => {featur} 0.1000000 0.7500000 1.406250 3
## [10162] {featur,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10163] {featur,
## paper,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10164] {featur,
## paper,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10165] {task,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [10166] {train,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [10167] {task,
## train,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10168] {task,
## train,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10169] {task,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10170] {learn,
## signific,
## effect} => {task} 0.1000000 0.7500000 2.045455 3
## [10171] {task,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10172] {task,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10173] {task,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [10174] {featur,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [10175] {featur,
## task,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10176] {featur,
## task,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10177] {train,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10178] {learn,
## signific,
## effect} => {train} 0.1000000 0.7500000 1.875000 3
## [10179] {train,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10180] {train,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10181] {train,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [10182] {featur,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [10183] {featur,
## train,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10184] {featur,
## train,
## signific} => {effect} 0.1000000 0.7500000 3.214286 3
## [10185] {learn,
## signific,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [10186] {represent,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10187] {represent,
## learn,
## effect} => {signific} 0.1000000 0.7500000 2.812500 3
## [10188] {represent,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10189] {learn,
## signific,
## effect} => {show} 0.1000000 0.7500000 1.406250 3
## [10190] {show,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10191] {show,
## learn,
## effect} => {signific} 0.1000000 0.7500000 2.812500 3
## [10192] {show,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10193] {learn,
## signific,
## effect} => {propos} 0.1000000 0.7500000 1.500000 3
## [10194] {propos,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10195] {propos,
## learn,
## effect} => {signific} 0.1000000 0.7500000 2.812500 3
## [10196] {propos,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10197] {learn,
## signific,
## effect} => {featur} 0.1000000 0.7500000 1.406250 3
## [10198] {featur,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10199] {featur,
## learn,
## effect} => {signific} 0.1000000 0.7500000 2.812500 3
## [10200] {featur,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10201] {method,
## effect,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [10202] {learn,
## effect,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [10203] {method,
## learn,
## effect} => {success} 0.1000000 1.0000000 3.750000 3
## [10204] {method,
## learn,
## success} => {effect} 0.1000000 0.7500000 3.214286 3
## [10205] {method,
## effect,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [10206] {represent,
## effect,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [10207] {method,
## represent,
## effect} => {success} 0.1000000 1.0000000 3.750000 3
## [10208] {method,
## represent,
## success} => {effect} 0.1000000 0.7500000 3.214286 3
## [10209] {method,
## effect,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [10210] {propos,
## effect,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [10211] {method,
## propos,
## effect} => {success} 0.1000000 1.0000000 3.750000 3
## [10212] {method,
## propos,
## success} => {effect} 0.1000000 0.7500000 3.214286 3
## [10213] {learn,
## effect,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [10214] {represent,
## effect,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [10215] {represent,
## learn,
## effect} => {success} 0.1000000 0.7500000 2.812500 3
## [10216] {learn,
## effect,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [10217] {propos,
## effect,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [10218] {propos,
## learn,
## effect} => {success} 0.1000000 0.7500000 2.812500 3
## [10219] {propos,
## learn,
## success} => {effect} 0.1000000 0.7500000 3.214286 3
## [10220] {represent,
## effect,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [10221] {propos,
## effect,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [10222] {represent,
## propos,
## effect} => {success} 0.1000000 1.0000000 3.750000 3
## [10223] {classif,
## learn,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [10224] {classif,
## propos,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10225] {propos,
## learn,
## effect} => {classif} 0.1000000 0.7500000 2.812500 3
## [10226] {classif,
## propos,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10227] {classif,
## learn,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [10228] {classif,
## featur,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10229] {featur,
## learn,
## effect} => {classif} 0.1000000 0.7500000 2.812500 3
## [10230] {classif,
## propos,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [10231] {classif,
## featur,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [10232] {featur,
## propos,
## effect} => {classif} 0.1000000 1.0000000 3.750000 3
## [10233] {perform,
## problem,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10234] {problem,
## learn,
## effect} => {perform} 0.1000000 1.0000000 2.142857 3
## [10235] {perform,
## learn,
## effect} => {problem} 0.1000000 1.0000000 3.333333 3
## [10236] {perform,
## problem,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [10237] {show,
## problem,
## effect} => {perform} 0.1000000 1.0000000 2.142857 3
## [10238] {show,
## perform,
## effect} => {problem} 0.1000000 1.0000000 3.333333 3
## [10239] {perform,
## problem,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [10240] {propos,
## problem,
## effect} => {perform} 0.1000000 1.0000000 2.142857 3
## [10241] {perform,
## propos,
## effect} => {problem} 0.1000000 1.0000000 3.333333 3
## [10242] {problem,
## learn,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [10243] {show,
## problem,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10244] {show,
## learn,
## effect} => {problem} 0.1000000 0.7500000 2.500000 3
## [10245] {show,
## problem,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10246] {problem,
## learn,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [10247] {propos,
## problem,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10248] {propos,
## learn,
## effect} => {problem} 0.1000000 0.7500000 2.500000 3
## [10249] {propos,
## problem,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10250] {show,
## problem,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [10251] {propos,
## problem,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [10252] {show,
## propos,
## effect} => {problem} 0.1000000 1.0000000 3.333333 3
## [10253] {show,
## propos,
## problem} => {effect} 0.1000000 0.7500000 3.214286 3
## [10254] {paper,
## task,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [10255] {paper,
## train,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [10256] {task,
## train,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10257] {paper,
## task,
## train} => {effect} 0.1000000 0.7500000 3.214286 3
## [10258] {paper,
## task,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10259] {paper,
## learn,
## effect} => {task} 0.1000000 0.7500000 2.045455 3
## [10260] {task,
## learn,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10261] {paper,
## task,
## learn} => {effect} 0.1000000 1.0000000 4.285714 3
## [10262] {paper,
## task,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [10263] {featur,
## paper,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [10264] {featur,
## task,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10265] {featur,
## paper,
## task} => {effect} 0.1000000 0.7500000 3.214286 3
## [10266] {paper,
## train,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10267] {paper,
## learn,
## effect} => {train} 0.1000000 0.7500000 1.875000 3
## [10268] {train,
## learn,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10269] {paper,
## train,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10270] {paper,
## train,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [10271] {featur,
## paper,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [10272] {featur,
## train,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10273] {paper,
## learn,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [10274] {paper,
## represent,
## effect} => {learn} 0.1000000 0.7500000 1.730769 3
## [10275] {represent,
## learn,
## effect} => {paper} 0.1000000 0.7500000 2.250000 3
## [10276] {paper,
## represent,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10277] {paper,
## learn,
## effect} => {show} 0.1000000 0.7500000 1.406250 3
## [10278] {paper,
## show,
## effect} => {learn} 0.1000000 0.7500000 1.730769 3
## [10279] {show,
## learn,
## effect} => {paper} 0.1000000 0.7500000 2.250000 3
## [10280] {paper,
## show,
## learn} => {effect} 0.1000000 1.0000000 4.285714 3
## [10281] {paper,
## learn,
## effect} => {propos} 0.1000000 0.7500000 1.500000 3
## [10282] {paper,
## propos,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10283] {propos,
## learn,
## effect} => {paper} 0.1000000 0.7500000 2.250000 3
## [10284] {paper,
## propos,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10285] {paper,
## learn,
## effect} => {featur} 0.1000000 0.7500000 1.406250 3
## [10286] {featur,
## paper,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10287] {featur,
## learn,
## effect} => {paper} 0.1000000 0.7500000 2.250000 3
## [10288] {featur,
## paper,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10289] {paper,
## represent,
## effect} => {show} 0.1000000 0.7500000 1.406250 3
## [10290] {paper,
## show,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [10291] {represent,
## show,
## effect} => {paper} 0.1000000 0.7500000 2.250000 3
## [10292] {paper,
## represent,
## show} => {effect} 0.1000000 0.7500000 3.214286 3
## [10293] {paper,
## represent,
## effect} => {network} 0.1000000 0.7500000 1.184211 3
## [10294] {network,
## paper,
## effect} => {represent} 0.1000000 1.0000000 2.000000 3
## [10295] {network,
## represent,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10296] {network,
## paper,
## represent} => {effect} 0.1000000 0.7500000 3.214286 3
## [10297] {paper,
## show,
## effect} => {model} 0.1000000 0.7500000 1.406250 3
## [10298] {model,
## paper,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [10299] {model,
## show,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10300] {method,
## learn,
## effect} => {represent} 0.1000000 1.0000000 2.000000 3
## [10301] {method,
## represent,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10302] {represent,
## learn,
## effect} => {method} 0.1000000 0.7500000 2.045455 3
## [10303] {method,
## represent,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10304] {method,
## learn,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [10305] {method,
## propos,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10306] {propos,
## learn,
## effect} => {method} 0.1000000 0.7500000 2.045455 3
## [10307] {method,
## propos,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10308] {method,
## represent,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [10309] {method,
## propos,
## effect} => {represent} 0.1000000 1.0000000 2.000000 3
## [10310] {represent,
## propos,
## effect} => {method} 0.1000000 1.0000000 2.727273 3
## [10311] {method,
## represent,
## propos} => {effect} 0.1000000 0.7500000 3.214286 3
## [10312] {task,
## train,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10313] {task,
## learn,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [10314] {train,
## learn,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [10315] {task,
## train,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10316] {task,
## train,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [10317] {featur,
## task,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [10318] {featur,
## train,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [10319] {featur,
## task,
## train} => {effect} 0.1000000 0.7500000 3.214286 3
## [10320] {task,
## learn,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [10321] {featur,
## task,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10322] {featur,
## learn,
## effect} => {task} 0.1000000 0.7500000 2.045455 3
## [10323] {train,
## learn,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [10324] {featur,
## train,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10325] {featur,
## learn,
## effect} => {train} 0.1000000 0.7500000 1.875000 3
## [10326] {featur,
## train,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10327] {perform,
## learn,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [10328] {show,
## perform,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10329] {show,
## learn,
## effect} => {perform} 0.1000000 0.7500000 1.607143 3
## [10330] {show,
## perform,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [10331] {perform,
## learn,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [10332] {perform,
## propos,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10333] {propos,
## learn,
## effect} => {perform} 0.1000000 0.7500000 1.607143 3
## [10334] {show,
## perform,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [10335] {perform,
## propos,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [10336] {show,
## propos,
## effect} => {perform} 0.1000000 1.0000000 2.142857 3
## [10337] {represent,
## learn,
## effect} => {show} 0.1000000 0.7500000 1.406250 3
## [10338] {show,
## learn,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [10339] {represent,
## show,
## effect} => {learn} 0.1000000 0.7500000 1.730769 3
## [10340] {represent,
## learn,
## effect} => {propos} 0.1000000 0.7500000 1.500000 3
## [10341] {propos,
## learn,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [10342] {represent,
## propos,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10343] {represent,
## learn,
## effect} => {featur} 0.1000000 0.7500000 1.406250 3
## [10344] {featur,
## learn,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [10345] {featur,
## represent,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10346] {show,
## learn,
## effect} => {propos} 0.1000000 0.7500000 1.500000 3
## [10347] {propos,
## learn,
## effect} => {show} 0.1000000 0.7500000 1.406250 3
## [10348] {show,
## propos,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10349] {show,
## learn,
## effect} => {featur} 0.1000000 0.7500000 1.406250 3
## [10350] {featur,
## learn,
## effect} => {show} 0.1000000 0.7500000 1.406250 3
## [10351] {featur,
## show,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10352] {propos,
## learn,
## effect} => {featur} 0.1000000 0.7500000 1.406250 3
## [10353] {featur,
## learn,
## effect} => {propos} 0.1000000 0.7500000 1.500000 3
## [10354] {featur,
## propos,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10355] {problem,
## applic,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [10356] {perform,
## applic,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [10357] {perform,
## problem,
## applic} => {success} 0.1000000 0.7500000 2.812500 3
## [10358] {perform,
## problem,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [10359] {problem,
## applic,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [10360] {represent,
## applic,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [10361] {represent,
## problem,
## applic} => {success} 0.1000000 0.7500000 2.812500 3
## [10362] {represent,
## problem,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [10363] {problem,
## applic,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [10364] {propos,
## applic,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [10365] {propos,
## problem,
## applic} => {success} 0.1000000 0.7500000 2.812500 3
## [10366] {propos,
## problem,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [10367] {perform,
## applic,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [10368] {represent,
## applic,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [10369] {represent,
## perform,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [10370] {perform,
## applic,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [10371] {propos,
## applic,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [10372] {perform,
## propos,
## applic} => {success} 0.1000000 0.7500000 2.812500 3
## [10373] {perform,
## propos,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [10374] {represent,
## applic,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [10375] {propos,
## applic,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [10376] {make,
## problem,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [10377] {approach,
## make,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [10378] {approach,
## problem,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [10379] {approach,
## make,
## problem} => {applic} 0.1000000 0.7500000 3.214286 3
## [10380] {make,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10381] {make,
## perform,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [10382] {perform,
## problem,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [10383] {make,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10384] {make,
## represent,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [10385] {represent,
## problem,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [10386] {make,
## represent,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [10387] {make,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10388] {make,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [10389] {propos,
## problem,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [10390] {make,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [10391] {approach,
## make,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10392] {make,
## perform,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [10393] {approach,
## perform,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [10394] {approach,
## make,
## perform} => {applic} 0.1000000 0.7500000 3.214286 3
## [10395] {approach,
## make,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10396] {make,
## represent,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [10397] {approach,
## represent,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [10398] {approach,
## make,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10399] {make,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [10400] {approach,
## propos,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [10401] {approach,
## make,
## propos} => {applic} 0.1000000 0.7500000 3.214286 3
## [10402] {make,
## perform,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [10403] {data,
## make,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10404] {data,
## perform,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [10405] {data,
## make,
## perform} => {applic} 0.1000000 0.7500000 3.214286 3
## [10406] {make,
## perform,
## applic} => {represent} 0.1000000 0.7500000 1.500000 3
## [10407] {make,
## represent,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10408] {make,
## represent,
## perform} => {applic} 0.1000000 0.7500000 3.214286 3
## [10409] {make,
## perform,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [10410] {make,
## propos,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10411] {perform,
## propos,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [10412] {make,
## perform,
## propos} => {applic} 0.1000000 0.7500000 3.214286 3
## [10413] {make,
## perform,
## applic} => {model} 0.1000000 0.7500000 1.406250 3
## [10414] {make,
## model,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10415] {model,
## perform,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [10416] {make,
## represent,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10417] {make,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10418] {approach,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10419] {perform,
## problem,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [10420] {approach,
## perform,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [10421] {approach,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10422] {represent,
## problem,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [10423] {approach,
## represent,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [10424] {approach,
## represent,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [10425] {approach,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10426] {propos,
## problem,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [10427] {approach,
## propos,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [10428] {approach,
## propos,
## problem} => {applic} 0.1000000 0.7500000 3.214286 3
## [10429] {perform,
## problem,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [10430] {problem,
## learn,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10431] {perform,
## learn,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [10432] {perform,
## problem,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [10433] {represent,
## problem,
## applic} => {perform} 0.1333333 1.0000000 2.142857 4
## [10434] {represent,
## perform,
## applic} => {problem} 0.1333333 0.8000000 2.666667 4
## [10435] {represent,
## perform,
## problem} => {applic} 0.1333333 1.0000000 4.285714 4
## [10436] {perform,
## problem,
## applic} => {propos} 0.1333333 1.0000000 2.000000 4
## [10437] {propos,
## problem,
## applic} => {perform} 0.1333333 1.0000000 2.142857 4
## [10438] {perform,
## propos,
## applic} => {problem} 0.1333333 1.0000000 3.333333 4
## [10439] {perform,
## problem,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [10440] {featur,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10441] {featur,
## perform,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [10442] {problem,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10443] {represent,
## problem,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [10444] {represent,
## learn,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [10445] {represent,
## problem,
## learn} => {applic} 0.1000000 1.0000000 4.285714 3
## [10446] {problem,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10447] {propos,
## problem,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [10448] {propos,
## learn,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [10449] {propos,
## problem,
## learn} => {applic} 0.1000000 0.7500000 3.214286 3
## [10450] {represent,
## problem,
## applic} => {propos} 0.1333333 1.0000000 2.000000 4
## [10451] {propos,
## problem,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [10452] {represent,
## propos,
## applic} => {problem} 0.1333333 0.8000000 2.666667 4
## [10453] {represent,
## propos,
## problem} => {applic} 0.1333333 1.0000000 4.285714 4
## [10454] {represent,
## problem,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [10455] {featur,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10456] {featur,
## represent,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [10457] {featur,
## represent,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [10458] {propos,
## problem,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [10459] {featur,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10460] {featur,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [10461] {featur,
## propos,
## problem} => {applic} 0.1000000 0.7500000 3.214286 3
## [10462] {result,
## work,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [10463] {data,
## result,
## applic} => {work} 0.1000000 0.7500000 1.875000 3
## [10464] {data,
## work,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [10465] {data,
## result,
## work} => {applic} 0.1000000 1.0000000 4.285714 3
## [10466] {result,
## work,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10467] {dataset,
## result,
## applic} => {work} 0.1000000 1.0000000 2.500000 3
## [10468] {dataset,
## work,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [10469] {dataset,
## result,
## work} => {applic} 0.1000000 1.0000000 4.285714 3
## [10470] {result,
## work,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10471] {represent,
## result,
## applic} => {work} 0.1000000 1.0000000 2.500000 3
## [10472] {represent,
## work,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [10473] {represent,
## result,
## work} => {applic} 0.1000000 1.0000000 4.285714 3
## [10474] {perform,
## result,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [10475] {data,
## result,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [10476] {data,
## perform,
## applic} => {result} 0.1000000 0.7500000 2.250000 3
## [10477] {data,
## perform,
## result} => {applic} 0.1000000 1.0000000 4.285714 3
## [10478] {data,
## result,
## applic} => {dataset} 0.1000000 0.7500000 1.730769 3
## [10479] {dataset,
## result,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [10480] {data,
## dataset,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [10481] {data,
## dataset,
## result} => {applic} 0.1000000 0.7500000 3.214286 3
## [10482] {data,
## result,
## applic} => {represent} 0.1000000 0.7500000 1.500000 3
## [10483] {represent,
## result,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [10484] {data,
## represent,
## applic} => {result} 0.1000000 0.7500000 2.250000 3
## [10485] {dataset,
## result,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10486] {represent,
## result,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10487] {represent,
## dataset,
## applic} => {result} 0.1000000 0.7500000 2.250000 3
## [10488] {represent,
## dataset,
## result} => {applic} 0.1000000 0.7500000 3.214286 3
## [10489] {method,
## perform,
## applic} => {show} 0.1000000 1.0000000 1.875000 3
## [10490] {method,
## show,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10491] {show,
## perform,
## applic} => {method} 0.1000000 1.0000000 2.727273 3
## [10492] {algorithm,
## perform,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [10493] {data,
## algorithm,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10494] {data,
## perform,
## applic} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [10495] {data,
## algorithm,
## perform} => {applic} 0.1000000 0.7500000 3.214286 3
## [10496] {approach,
## task,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [10497] {data,
## task,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [10498] {approach,
## data,
## applic} => {task} 0.1000000 1.0000000 2.727273 3
## [10499] {approach,
## task,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10500] {represent,
## task,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [10501] {approach,
## represent,
## applic} => {task} 0.1000000 0.7500000 2.045455 3
## [10502] {approach,
## task,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10503] {task,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [10504] {approach,
## propos,
## applic} => {task} 0.1000000 0.7500000 2.045455 3
## [10505] {data,
## task,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10506] {represent,
## task,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [10507] {data,
## represent,
## applic} => {task} 0.1000000 0.7500000 2.045455 3
## [10508] {data,
## task,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10509] {task,
## propos,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [10510] {data,
## propos,
## applic} => {task} 0.1000000 1.0000000 2.727273 3
## [10511] {represent,
## task,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10512] {task,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10513] {approach,
## perform,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10514] {approach,
## represent,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [10515] {approach,
## represent,
## perform} => {applic} 0.1000000 1.0000000 4.285714 3
## [10516] {approach,
## perform,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10517] {approach,
## propos,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [10518] {perform,
## propos,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [10519] {approach,
## data,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10520] {approach,
## represent,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [10521] {data,
## represent,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [10522] {approach,
## data,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10523] {approach,
## propos,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [10524] {data,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [10525] {approach,
## data,
## propos} => {applic} 0.1000000 0.7500000 3.214286 3
## [10526] {approach,
## dataset,
## applic} => {learn} 0.1000000 1.0000000 2.307692 3
## [10527] {approach,
## learn,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10528] {dataset,
## learn,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [10529] {approach,
## dataset,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10530] {approach,
## represent,
## applic} => {dataset} 0.1000000 0.7500000 1.730769 3
## [10531] {represent,
## dataset,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [10532] {approach,
## represent,
## dataset} => {applic} 0.1000000 0.7500000 3.214286 3
## [10533] {approach,
## dataset,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10534] {approach,
## propos,
## applic} => {dataset} 0.1000000 0.7500000 1.730769 3
## [10535] {dataset,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [10536] {approach,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10537] {approach,
## represent,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [10538] {represent,
## learn,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [10539] {approach,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10540] {approach,
## propos,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [10541] {propos,
## learn,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [10542] {approach,
## represent,
## applic} => {propos} 0.1333333 1.0000000 2.000000 4
## [10543] {approach,
## propos,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [10544] {represent,
## propos,
## applic} => {approach} 0.1333333 0.8000000 2.000000 4
## [10545] {data,
## work,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10546] {dataset,
## work,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [10547] {data,
## dataset,
## applic} => {work} 0.1000000 1.0000000 2.500000 3
## [10548] {data,
## work,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10549] {represent,
## work,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [10550] {data,
## represent,
## applic} => {work} 0.1000000 0.7500000 1.875000 3
## [10551] {data,
## represent,
## work} => {applic} 0.1000000 0.7500000 3.214286 3
## [10552] {dataset,
## work,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10553] {represent,
## work,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10554] {represent,
## dataset,
## applic} => {work} 0.1000000 0.7500000 1.875000 3
## [10555] {represent,
## dataset,
## work} => {applic} 0.1000000 0.7500000 3.214286 3
## [10556] {data,
## perform,
## applic} => {represent} 0.1000000 0.7500000 1.500000 3
## [10557] {data,
## represent,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [10558] {data,
## represent,
## perform} => {applic} 0.1000000 0.7500000 3.214286 3
## [10559] {data,
## perform,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [10560] {featur,
## perform,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [10561] {data,
## featur,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10562] {dataset,
## perform,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10563] {represent,
## dataset,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [10564] {represent,
## dataset,
## perform} => {applic} 0.1000000 0.7500000 3.214286 3
## [10565] {perform,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10566] {represent,
## learn,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [10567] {represent,
## perform,
## learn} => {applic} 0.1000000 0.7500000 3.214286 3
## [10568] {perform,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10569] {perform,
## propos,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [10570] {propos,
## learn,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [10571] {represent,
## perform,
## applic} => {propos} 0.1333333 0.8000000 1.600000 4
## [10572] {perform,
## propos,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [10573] {represent,
## propos,
## applic} => {perform} 0.1333333 0.8000000 1.714286 4
## [10574] {represent,
## perform,
## applic} => {featur} 0.1333333 0.8000000 1.500000 4
## [10575] {featur,
## perform,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [10576] {featur,
## represent,
## applic} => {perform} 0.1333333 1.0000000 2.142857 4
## [10577] {featur,
## represent,
## perform} => {applic} 0.1333333 0.8000000 3.428571 4
## [10578] {perform,
## propos,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [10579] {featur,
## perform,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [10580] {featur,
## propos,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [10581] {data,
## dataset,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10582] {data,
## represent,
## applic} => {dataset} 0.1000000 0.7500000 1.730769 3
## [10583] {represent,
## dataset,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [10584] {data,
## represent,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [10585] {data,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10586] {data,
## represent,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [10587] {data,
## featur,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10588] {featur,
## represent,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [10589] {dataset,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10590] {represent,
## dataset,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [10591] {represent,
## learn,
## applic} => {dataset} 0.1000000 0.7500000 1.730769 3
## [10592] {dataset,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10593] {dataset,
## propos,
## applic} => {learn} 0.1000000 1.0000000 2.307692 3
## [10594] {propos,
## learn,
## applic} => {dataset} 0.1000000 0.7500000 1.730769 3
## [10595] {represent,
## dataset,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [10596] {dataset,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10597] {represent,
## learn,
## applic} => {show} 0.1000000 0.7500000 1.406250 3
## [10598] {show,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10599] {represent,
## show,
## applic} => {learn} 0.1000000 1.0000000 2.307692 3
## [10600] {represent,
## learn,
## applic} => {propos} 0.1333333 1.0000000 2.000000 4
## [10601] {propos,
## learn,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [10602] {represent,
## propos,
## applic} => {learn} 0.1333333 0.8000000 1.846154 4
## [10603] {show,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10604] {propos,
## learn,
## applic} => {show} 0.1000000 0.7500000 1.406250 3
## [10605] {show,
## propos,
## applic} => {learn} 0.1000000 1.0000000 2.307692 3
## [10606] {represent,
## show,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [10607] {show,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10608] {featur,
## represent,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [10609] {featur,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [10610] {approach,
## input,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10611] {input,
## learn,
## recent} => {approach} 0.1000000 1.0000000 2.500000 3
## [10612] {approach,
## input,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [10613] {approach,
## learn,
## recent} => {input} 0.1000000 1.0000000 4.285714 3
## [10614] {approach,
## input,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10615] {input,
## show,
## recent} => {approach} 0.1000000 1.0000000 2.500000 3
## [10616] {approach,
## input,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [10617] {approach,
## show,
## recent} => {input} 0.1000000 1.0000000 4.285714 3
## [10618] {approach,
## input,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10619] {input,
## model,
## recent} => {approach} 0.1000000 1.0000000 2.500000 3
## [10620] {approach,
## input,
## model} => {recent} 0.1000000 0.7500000 3.214286 3
## [10621] {approach,
## model,
## recent} => {input} 0.1000000 1.0000000 4.285714 3
## [10622] {input,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10623] {input,
## show,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10624] {input,
## show,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [10625] {input,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10626] {input,
## model,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10627] {input,
## model,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [10628] {input,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10629] {input,
## model,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10630] {input,
## make,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [10631] {approach,
## input,
## make} => {problem} 0.1000000 0.7500000 2.500000 3
## [10632] {approach,
## input,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [10633] {approach,
## make,
## problem} => {input} 0.1000000 0.7500000 3.214286 3
## [10634] {input,
## make,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [10635] {input,
## make,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [10636] {input,
## perform,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [10637] {input,
## make,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [10638] {approach,
## input,
## make} => {method} 0.1000000 0.7500000 2.045455 3
## [10639] {approach,
## input,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [10640] {approach,
## make,
## method} => {input} 0.1000000 0.7500000 3.214286 3
## [10641] {input,
## make,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [10642] {input,
## make,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [10643] {input,
## method,
## show} => {make} 0.1000000 1.0000000 3.333333 3
## [10644] {make,
## method,
## show} => {input} 0.1000000 0.7500000 3.214286 3
## [10645] {input,
## make,
## method} => {model} 0.1000000 1.0000000 1.875000 3
## [10646] {input,
## make,
## model} => {method} 0.1000000 1.0000000 2.727273 3
## [10647] {input,
## method,
## model} => {make} 0.1000000 1.0000000 3.333333 3
## [10648] {make,
## method,
## model} => {input} 0.1000000 0.7500000 3.214286 3
## [10649] {approach,
## input,
## make} => {perform} 0.1000000 0.7500000 1.607143 3
## [10650] {input,
## make,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [10651] {approach,
## input,
## perform} => {make} 0.1000000 1.0000000 3.333333 3
## [10652] {approach,
## make,
## perform} => {input} 0.1000000 0.7500000 3.214286 3
## [10653] {approach,
## input,
## make} => {represent} 0.1000000 0.7500000 1.500000 3
## [10654] {input,
## make,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [10655] {approach,
## input,
## represent} => {make} 0.1000000 0.7500000 2.500000 3
## [10656] {approach,
## input,
## make} => {show} 0.1000000 0.7500000 1.406250 3
## [10657] {input,
## make,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [10658] {approach,
## input,
## show} => {make} 0.1000000 0.7500000 2.500000 3
## [10659] {approach,
## make,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [10660] {approach,
## input,
## make} => {model} 0.1000000 0.7500000 1.406250 3
## [10661] {input,
## make,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [10662] {approach,
## input,
## model} => {make} 0.1000000 0.7500000 2.500000 3
## [10663] {approach,
## make,
## model} => {input} 0.1000000 0.7500000 3.214286 3
## [10664] {approach,
## input,
## make} => {featur} 0.1000000 0.7500000 1.406250 3
## [10665] {featur,
## input,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [10666] {approach,
## featur,
## input} => {make} 0.1000000 0.7500000 2.500000 3
## [10667] {input,
## make,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10668] {input,
## make,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10669] {make,
## model,
## show} => {input} 0.1000000 0.7500000 3.214286 3
## [10670] {approach,
## input,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [10671] {input,
## perform,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [10672] {approach,
## input,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [10673] {input,
## paper,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [10674] {input,
## paper,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [10675] {input,
## represent,
## show} => {paper} 0.1000000 0.7500000 2.250000 3
## [10676] {paper,
## represent,
## show} => {input} 0.1000000 0.7500000 3.214286 3
## [10677] {input,
## paper,
## represent} => {model} 0.1000000 1.0000000 1.875000 3
## [10678] {input,
## model,
## paper} => {represent} 0.1000000 1.0000000 2.000000 3
## [10679] {input,
## model,
## represent} => {paper} 0.1000000 0.7500000 2.250000 3
## [10680] {model,
## paper,
## represent} => {input} 0.1000000 0.7500000 3.214286 3
## [10681] {input,
## paper,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10682] {input,
## model,
## paper} => {show} 0.1000000 1.0000000 1.875000 3
## [10683] {approach,
## input,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [10684] {input,
## method,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [10685] {approach,
## input,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [10686] {approach,
## input,
## method} => {model} 0.1000000 1.0000000 1.875000 3
## [10687] {input,
## method,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [10688] {approach,
## input,
## model} => {method} 0.1000000 0.7500000 2.045455 3
## [10689] {input,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10690] {input,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10691] {approach,
## input,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [10692] {approach,
## input,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [10693] {input,
## show,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [10694] {approach,
## input,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [10695] {approach,
## input,
## model} => {learn} 0.1000000 0.7500000 1.730769 3
## [10696] {input,
## model,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [10697] {approach,
## input,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [10698] {approach,
## input,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [10699] {input,
## represent,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [10700] {approach,
## input,
## represent} => {model} 0.1000000 0.7500000 1.406250 3
## [10701] {approach,
## input,
## model} => {represent} 0.1000000 0.7500000 1.500000 3
## [10702] {input,
## model,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [10703] {approach,
## input,
## represent} => {featur} 0.1000000 0.7500000 1.406250 3
## [10704] {approach,
## featur,
## input} => {represent} 0.1000000 0.7500000 1.500000 3
## [10705] {featur,
## input,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [10706] {approach,
## input,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [10707] {approach,
## input,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [10708] {input,
## model,
## show} => {approach} 0.1333333 0.8000000 2.000000 4
## [10709] {approach,
## input,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [10710] {approach,
## featur,
## input} => {show} 0.1000000 0.7500000 1.406250 3
## [10711] {featur,
## input,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [10712] {approach,
## input,
## model} => {featur} 0.1000000 0.7500000 1.406250 3
## [10713] {approach,
## featur,
## input} => {model} 0.1000000 0.7500000 1.406250 3
## [10714] {featur,
## input,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [10715] {input,
## show,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [10716] {input,
## model,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [10717] {input,
## represent,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [10718] {input,
## model,
## represent} => {show} 0.1333333 1.0000000 1.875000 4
## [10719] {input,
## model,
## show} => {represent} 0.1333333 0.8000000 1.600000 4
## [10720] {input,
## represent,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [10721] {input,
## network,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [10722] {input,
## network,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [10723] {input,
## model,
## represent} => {network} 0.1000000 0.7500000 1.184211 3
## [10724] {input,
## network,
## represent} => {model} 0.1000000 1.0000000 1.875000 3
## [10725] {input,
## model,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [10726] {model,
## network,
## represent} => {input} 0.1000000 0.7500000 3.214286 3
## [10727] {featur,
## input,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10728] {featur,
## input,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10729] {input,
## network,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10730] {input,
## model,
## network} => {show} 0.1000000 1.0000000 1.875000 3
## [10731] {reduc,
## comput,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [10732] {reduc,
## comput,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [10733] {comput,
## optim,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [10734] {reduc,
## optim,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [10735] {reduc,
## comput,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [10736] {reduc,
## improv,
## comput} => {optim} 0.1000000 1.0000000 4.285714 3
## [10737] {improv,
## comput,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [10738] {reduc,
## improv,
## optim} => {comput} 0.1000000 1.0000000 4.285714 3
## [10739] {reduc,
## comput,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [10740] {reduc,
## improv,
## comput} => {problem} 0.1000000 1.0000000 3.333333 3
## [10741] {improv,
## comput,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [10742] {reduc,
## improv,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [10743] {comput,
## optim,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [10744] {improv,
## comput,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [10745] {improv,
## comput,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [10746] {improv,
## optim,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [10747] {algorithm,
## improv,
## comput} => {train} 0.1000000 1.0000000 2.500000 3
## [10748] {train,
## improv,
## comput} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [10749] {train,
## algorithm,
## comput} => {improv} 0.1000000 1.0000000 3.333333 3
## [10750] {train,
## algorithm,
## improv} => {comput} 0.1000000 0.7500000 3.214286 3
## [10751] {machin,
## problem,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10752] {machin,
## show,
## recent} => {problem} 0.1000000 0.7500000 2.500000 3
## [10753] {show,
## problem,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [10754] {machin,
## show,
## problem} => {recent} 0.1000000 1.0000000 4.285714 3
## [10755] {machin,
## problem,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10756] {machin,
## model,
## recent} => {problem} 0.1000000 0.7500000 2.500000 3
## [10757] {model,
## problem,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [10758] {machin,
## model,
## problem} => {recent} 0.1000000 1.0000000 4.285714 3
## [10759] {machin,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10760] {machin,
## show,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [10761] {show,
## algorithm,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [10762] {machin,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [10763] {machin,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10764] {machin,
## model,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [10765] {model,
## algorithm,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [10766] {machin,
## model,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [10767] {machin,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10768] {machin,
## show,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [10769] {machin,
## show,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [10770] {machin,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10771] {machin,
## model,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [10772] {machin,
## model,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [10773] {machin,
## learn,
## recent} => {featur} 0.1000000 1.0000000 1.875000 3
## [10774] {featur,
## machin,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10775] {featur,
## learn,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [10776] {featur,
## machin,
## learn} => {recent} 0.1000000 0.7500000 3.214286 3
## [10777] {machin,
## show,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [10778] {machin,
## model,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [10779] {machin,
## model,
## show} => {recent} 0.1333333 0.8000000 3.428571 4
## [10780] {machin,
## show,
## recent} => {featur} 0.1000000 0.7500000 1.406250 3
## [10781] {featur,
## machin,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10782] {featur,
## show,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [10783] {featur,
## machin,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [10784] {machin,
## model,
## recent} => {featur} 0.1000000 0.7500000 1.406250 3
## [10785] {featur,
## machin,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10786] {featur,
## model,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [10787] {featur,
## machin,
## model} => {recent} 0.1000000 0.7500000 3.214286 3
## [10788] {problem,
## recent,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [10789] {show,
## recent,
## signific} => {problem} 0.1000000 1.0000000 3.333333 3
## [10790] {show,
## problem,
## recent} => {signific} 0.1000000 0.7500000 2.812500 3
## [10791] {show,
## problem,
## signific} => {recent} 0.1000000 1.0000000 4.285714 3
## [10792] {problem,
## recent,
## signific} => {model} 0.1000000 1.0000000 1.875000 3
## [10793] {model,
## recent,
## signific} => {problem} 0.1000000 1.0000000 3.333333 3
## [10794] {model,
## problem,
## recent} => {signific} 0.1000000 0.7500000 2.812500 3
## [10795] {model,
## problem,
## signific} => {recent} 0.1000000 1.0000000 4.285714 3
## [10796] {show,
## recent,
## signific} => {model} 0.1000000 1.0000000 1.875000 3
## [10797] {model,
## recent,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [10798] {model,
## show,
## signific} => {recent} 0.1000000 1.0000000 4.285714 3
## [10799] {method,
## problem,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10800] {show,
## problem,
## recent} => {method} 0.1000000 0.7500000 2.045455 3
## [10801] {method,
## show,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [10802] {method,
## problem,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10803] {model,
## problem,
## recent} => {method} 0.1000000 0.7500000 2.045455 3
## [10804] {method,
## model,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [10805] {method,
## model,
## problem} => {recent} 0.1000000 1.0000000 4.285714 3
## [10806] {perform,
## problem,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10807] {problem,
## learn,
## recent} => {perform} 0.1000000 1.0000000 2.142857 3
## [10808] {perform,
## learn,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [10809] {perform,
## problem,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10810] {show,
## problem,
## recent} => {perform} 0.1000000 0.7500000 1.607143 3
## [10811] {show,
## perform,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [10812] {perform,
## problem,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10813] {model,
## problem,
## recent} => {perform} 0.1000000 0.7500000 1.607143 3
## [10814] {model,
## perform,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [10815] {problem,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10816] {show,
## problem,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [10817] {show,
## problem,
## learn} => {recent} 0.1000000 0.7500000 3.214286 3
## [10818] {problem,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10819] {model,
## problem,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [10820] {model,
## problem,
## learn} => {recent} 0.1000000 0.7500000 3.214286 3
## [10821] {show,
## problem,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [10822] {model,
## problem,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [10823] {model,
## show,
## problem} => {recent} 0.1333333 1.0000000 4.285714 4
## [10824] {method,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10825] {method,
## model,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10826] {algorithm,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10827] {show,
## algorithm,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [10828] {show,
## algorithm,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [10829] {algorithm,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10830] {model,
## algorithm,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [10831] {model,
## algorithm,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [10832] {algorithm,
## learn,
## recent} => {featur} 0.1000000 1.0000000 1.875000 3
## [10833] {featur,
## algorithm,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10834] {featur,
## learn,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [10835] {featur,
## algorithm,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [10836] {show,
## algorithm,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [10837] {model,
## algorithm,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [10838] {show,
## algorithm,
## recent} => {featur} 0.1000000 0.7500000 1.406250 3
## [10839] {featur,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10840] {featur,
## show,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [10841] {featur,
## show,
## algorithm} => {recent} 0.1000000 0.7500000 3.214286 3
## [10842] {model,
## algorithm,
## recent} => {featur} 0.1000000 0.7500000 1.406250 3
## [10843] {featur,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10844] {featur,
## model,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [10845] {featur,
## model,
## algorithm} => {recent} 0.1000000 0.7500000 3.214286 3
## [10846] {approach,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10847] {approach,
## show,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10848] {approach,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10849] {approach,
## model,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10850] {approach,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10851] {approach,
## model,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10852] {perform,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10853] {show,
## perform,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10854] {show,
## perform,
## learn} => {recent} 0.1000000 0.7500000 3.214286 3
## [10855] {perform,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10856] {model,
## perform,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10857] {show,
## perform,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10858] {model,
## perform,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10859] {represent,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10860] {represent,
## show,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10861] {represent,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10862] {model,
## represent,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [10863] {show,
## learn,
## recent} => {model} 0.1666667 1.0000000 1.875000 5
## [10864] {model,
## learn,
## recent} => {show} 0.1666667 1.0000000 1.875000 5
## [10865] {model,
## show,
## recent} => {learn} 0.1666667 0.8333333 1.923077 5
## [10866] {model,
## show,
## learn} => {recent} 0.1666667 0.8333333 3.571429 5
## [10867] {show,
## learn,
## recent} => {featur} 0.1333333 0.8000000 1.500000 4
## [10868] {featur,
## learn,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [10869] {featur,
## show,
## recent} => {learn} 0.1333333 1.0000000 2.307692 4
## [10870] {model,
## learn,
## recent} => {featur} 0.1333333 0.8000000 1.500000 4
## [10871] {featur,
## learn,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [10872] {featur,
## model,
## recent} => {learn} 0.1333333 1.0000000 2.307692 4
## [10873] {represent,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10874] {model,
## represent,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10875] {featur,
## show,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [10876] {featur,
## model,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [10877] {classif,
## machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [10878] {machin,
## make,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [10879] {classif,
## machin,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [10880] {classif,
## make,
## method} => {machin} 0.1000000 1.0000000 4.285714 3
## [10881] {classif,
## machin,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [10882] {approach,
## machin,
## make} => {classif} 0.1000000 1.0000000 3.750000 3
## [10883] {approach,
## classif,
## machin} => {make} 0.1000000 1.0000000 3.333333 3
## [10884] {approach,
## classif,
## make} => {machin} 0.1000000 1.0000000 4.285714 3
## [10885] {classif,
## machin,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [10886] {featur,
## machin,
## make} => {classif} 0.1000000 1.0000000 3.750000 3
## [10887] {classif,
## featur,
## machin} => {make} 0.1000000 0.7500000 2.500000 3
## [10888] {classif,
## featur,
## make} => {machin} 0.1000000 1.0000000 4.285714 3
## [10889] {machin,
## make,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [10890] {approach,
## machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [10891] {approach,
## machin,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [10892] {approach,
## make,
## method} => {machin} 0.1000000 0.7500000 3.214286 3
## [10893] {machin,
## make,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [10894] {featur,
## machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [10895] {featur,
## machin,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [10896] {featur,
## make,
## method} => {machin} 0.1000000 1.0000000 4.285714 3
## [10897] {approach,
## machin,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [10898] {featur,
## machin,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [10899] {approach,
## featur,
## machin} => {make} 0.1000000 1.0000000 3.333333 3
## [10900] {classif,
## machin,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [10901] {classif,
## machin,
## task} => {paper} 0.1000000 1.0000000 3.000000 3
## [10902] {machin,
## paper,
## task} => {classif} 0.1000000 1.0000000 3.750000 3
## [10903] {classif,
## paper,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [10904] {classif,
## machin,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [10905] {classif,
## machin,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [10906] {machin,
## paper,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [10907] {classif,
## paper,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [10908] {classif,
## machin,
## paper} => {featur} 0.1000000 1.0000000 1.875000 3
## [10909] {classif,
## featur,
## machin} => {paper} 0.1000000 0.7500000 2.250000 3
## [10910] {featur,
## machin,
## paper} => {classif} 0.1000000 1.0000000 3.750000 3
## [10911] {classif,
## featur,
## paper} => {machin} 0.1000000 1.0000000 4.285714 3
## [10912] {classif,
## machin,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [10913] {approach,
## classif,
## machin} => {method} 0.1000000 1.0000000 2.727273 3
## [10914] {approach,
## machin,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [10915] {classif,
## machin,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [10916] {classif,
## featur,
## machin} => {method} 0.1000000 0.7500000 2.045455 3
## [10917] {featur,
## machin,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [10918] {classif,
## machin,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [10919] {classif,
## machin,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [10920] {machin,
## task,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [10921] {classif,
## task,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [10922] {classif,
## machin,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10923] {classif,
## featur,
## machin} => {task} 0.1000000 0.7500000 2.045455 3
## [10924] {featur,
## machin,
## task} => {classif} 0.1000000 0.7500000 2.812500 3
## [10925] {classif,
## featur,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [10926] {classif,
## machin,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [10927] {classif,
## featur,
## machin} => {train} 0.1000000 0.7500000 1.875000 3
## [10928] {featur,
## machin,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [10929] {classif,
## featur,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [10930] {approach,
## classif,
## machin} => {featur} 0.1000000 1.0000000 1.875000 3
## [10931] {classif,
## featur,
## machin} => {approach} 0.1000000 0.7500000 1.875000 3
## [10932] {approach,
## featur,
## machin} => {classif} 0.1000000 1.0000000 3.750000 3
## [10933] {approach,
## classif,
## featur} => {machin} 0.1000000 0.7500000 3.214286 3
## [10934] {classif,
## machin,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [10935] {classif,
## featur,
## machin} => {learn} 0.1000000 0.7500000 1.730769 3
## [10936] {featur,
## machin,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [10937] {classif,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10938] {classif,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10939] {classif,
## model,
## show} => {machin} 0.1000000 0.7500000 3.214286 3
## [10940] {classif,
## machin,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10941] {classif,
## featur,
## machin} => {show} 0.1000000 0.7500000 1.406250 3
## [10942] {featur,
## machin,
## show} => {classif} 0.1000000 0.7500000 2.812500 3
## [10943] {classif,
## machin,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [10944] {classif,
## featur,
## machin} => {model} 0.1000000 0.7500000 1.406250 3
## [10945] {featur,
## machin,
## model} => {classif} 0.1000000 0.7500000 2.812500 3
## [10946] {machin,
## show,
## problem} => {model} 0.1000000 1.0000000 1.875000 3
## [10947] {machin,
## model,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [10948] {model,
## show,
## problem} => {machin} 0.1000000 0.7500000 3.214286 3
## [10949] {machin,
## paper,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [10950] {machin,
## paper,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [10951] {machin,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [10952] {paper,
## task,
## train} => {machin} 0.1000000 0.7500000 3.214286 3
## [10953] {machin,
## paper,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10954] {featur,
## machin,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [10955] {featur,
## machin,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [10956] {featur,
## paper,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [10957] {machin,
## paper,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [10958] {featur,
## machin,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [10959] {featur,
## machin,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [10960] {approach,
## machin,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [10961] {featur,
## machin,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [10962] {approach,
## featur,
## machin} => {method} 0.1000000 1.0000000 2.727273 3
## [10963] {machin,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10964] {machin,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10965] {machin,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [10966] {machin,
## model,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [10967] {machin,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [10968] {featur,
## machin,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [10969] {featur,
## machin,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [10970] {featur,
## task,
## train} => {machin} 0.1000000 0.7500000 3.214286 3
## [10971] {data,
## machin,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [10972] {machin,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [10973] {data,
## machin,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [10974] {data,
## machin,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [10975] {machin,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [10976] {data,
## machin,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [10977] {data,
## machin,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10978] {featur,
## machin,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [10979] {data,
## featur,
## machin} => {task} 0.1000000 1.0000000 2.727273 3
## [10980] {machin,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [10981] {featur,
## machin,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [10982] {featur,
## machin,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [10983] {machin,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10984] {featur,
## machin,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [10985] {featur,
## machin,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [10986] {machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [10987] {machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [10988] {model,
## show,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [10989] {machin,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10990] {featur,
## machin,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [10991] {featur,
## machin,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [10992] {machin,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10993] {featur,
## machin,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [10994] {featur,
## machin,
## model} => {task} 0.1000000 0.7500000 2.045455 3
## [10995] {machin,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [10996] {machin,
## model,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [10997] {model,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [10998] {data,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10999] {data,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [11000] {data,
## machin,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [11001] {data,
## featur,
## machin} => {show} 0.1000000 1.0000000 1.875000 3
## [11002] {featur,
## machin,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [11003] {data,
## machin,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [11004] {data,
## featur,
## machin} => {model} 0.1000000 1.0000000 1.875000 3
## [11005] {featur,
## machin,
## model} => {data} 0.1000000 0.7500000 1.730769 3
## [11006] {machin,
## show,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [11007] {machin,
## model,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [11008] {machin,
## show,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11009] {featur,
## machin,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [11010] {featur,
## machin,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [11011] {machin,
## model,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11012] {featur,
## machin,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [11013] {featur,
## machin,
## model} => {learn} 0.1000000 0.7500000 1.730769 3
## [11014] {machin,
## model,
## show} => {featur} 0.1333333 0.8000000 1.500000 4
## [11015] {featur,
## machin,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [11016] {featur,
## machin,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [11017] {architectur,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [11018] {classif,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11019] {classif,
## architectur,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [11020] {classif,
## architectur,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [11021] {architectur,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11022] {dataset,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11023] {architectur,
## dataset,
## process} => {experi} 0.1000000 0.7500000 2.812500 3
## [11024] {architectur,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [11025] {architectur,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11026] {experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11027] {architectur,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [11028] {architectur,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11029] {architectur,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11030] {network,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11031] {network,
## architectur,
## process} => {experi} 0.1000000 0.7500000 2.812500 3
## [11032] {network,
## architectur,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [11033] {classif,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11034] {dataset,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [11035] {classif,
## dataset,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [11036] {classif,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [11037] {classif,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11038] {experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [11039] {classif,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [11040] {classif,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11041] {classif,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11042] {network,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [11043] {classif,
## network,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [11044] {classif,
## network,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [11045] {dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11046] {experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11047] {dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [11048] {dataset,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11049] {dataset,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11050] {network,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11051] {network,
## dataset,
## process} => {experi} 0.1000000 0.7500000 2.812500 3
## [11052] {network,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [11053] {experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [11054] {network,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11055] {network,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [11056] {network,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11057] {classif,
## architectur,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11058] {architectur,
## dataset,
## process} => {classif} 0.1000000 0.7500000 2.812500 3
## [11059] {classif,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11060] {classif,
## architectur,
## dataset} => {process} 0.1000000 1.0000000 5.000000 3
## [11061] {classif,
## architectur,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11062] {architectur,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [11063] {classif,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11064] {classif,
## architectur,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11065] {classif,
## architectur,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11066] {network,
## architectur,
## process} => {classif} 0.1000000 0.7500000 2.812500 3
## [11067] {classif,
## network,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11068] {classif,
## network,
## architectur} => {process} 0.1000000 0.7500000 3.750000 3
## [11069] {architectur,
## process,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [11070] {model,
## architectur,
## process} => {recognit} 0.1000000 1.0000000 3.333333 3
## [11071] {model,
## process,
## recognit} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11072] {model,
## architectur,
## recognit} => {process} 0.1000000 1.0000000 5.000000 3
## [11073] {architectur,
## process,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [11074] {featur,
## architectur,
## process} => {recognit} 0.1000000 0.7500000 2.500000 3
## [11075] {featur,
## process,
## recognit} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11076] {featur,
## architectur,
## recognit} => {process} 0.1000000 1.0000000 5.000000 3
## [11077] {architectur,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11078] {architectur,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11079] {improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11080] {architectur,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [11081] {architectur,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11082] {algorithm,
## architectur,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11083] {algorithm,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11084] {algorithm,
## architectur,
## improv} => {process} 0.1000000 1.0000000 5.000000 3
## [11085] {architectur,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11086] {architectur,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11087] {improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11088] {architectur,
## improv,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11089] {architectur,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11090] {architectur,
## dataset,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11091] {dataset,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11092] {architectur,
## dataset,
## improv} => {process} 0.1000000 1.0000000 5.000000 3
## [11093] {architectur,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11094] {network,
## architectur,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11095] {network,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11096] {network,
## architectur,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [11097] {architectur,
## process,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11098] {algorithm,
## architectur,
## process} => {result} 0.1000000 0.7500000 2.250000 3
## [11099] {algorithm,
## process,
## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11100] {algorithm,
## architectur,
## result} => {process} 0.1000000 1.0000000 5.000000 3
## [11101] {architectur,
## process,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [11102] {featur,
## architectur,
## process} => {result} 0.1000000 0.7500000 2.250000 3
## [11103] {featur,
## process,
## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11104] {featur,
## architectur,
## result} => {process} 0.1000000 0.7500000 3.750000 3
## [11105] {architectur,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11106] {algorithm,
## architectur,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11107] {algorithm,
## neural,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11108] {algorithm,
## architectur,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [11109] {architectur,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11110] {architectur,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11111] {neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11112] {architectur,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11113] {architectur,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11114] {architectur,
## dataset,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11115] {dataset,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11116] {architectur,
## dataset,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [11117] {architectur,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11118] {network,
## architectur,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11119] {network,
## neural,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11120] {network,
## architectur,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11121] {algorithm,
## architectur,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11122] {architectur,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11123] {algorithm,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11124] {algorithm,
## architectur,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11125] {algorithm,
## architectur,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11126] {architectur,
## dataset,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11127] {algorithm,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11128] {algorithm,
## architectur,
## dataset} => {process} 0.1000000 1.0000000 5.000000 3
## [11129] {algorithm,
## architectur,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11130] {show,
## architectur,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11131] {show,
## algorithm,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11132] {show,
## algorithm,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [11133] {algorithm,
## architectur,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11134] {featur,
## architectur,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11135] {featur,
## algorithm,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11136] {featur,
## algorithm,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [11137] {algorithm,
## architectur,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [11138] {network,
## architectur,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11139] {network,
## algorithm,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11140] {network,
## algorithm,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [11141] {architectur,
## process,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11142] {architectur,
## dataset,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11143] {dataset,
## process,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11144] {architectur,
## dataset,
## work} => {process} 0.1000000 0.7500000 3.750000 3
## [11145] {architectur,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11146] {network,
## architectur,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11147] {network,
## process,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11148] {architectur,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11149] {architectur,
## dataset,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11150] {dataset,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11151] {architectur,
## dataset,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11152] {architectur,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11153] {network,
## architectur,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11154] {network,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11155] {data,
## architectur,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11156] {featur,
## architectur,
## process} => {data} 0.1000000 0.7500000 1.730769 3
## [11157] {data,
## featur,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11158] {data,
## featur,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [11159] {architectur,
## dataset,
## process} => {propos} 0.1000000 0.7500000 1.500000 3
## [11160] {architectur,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11161] {dataset,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11162] {architectur,
## dataset,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11163] {architectur,
## dataset,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11164] {featur,
## architectur,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11165] {featur,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11166] {featur,
## architectur,
## dataset} => {process} 0.1000000 0.7500000 3.750000 3
## [11167] {architectur,
## dataset,
## process} => {network} 0.1333333 1.0000000 1.578947 4
## [11168] {network,
## architectur,
## process} => {dataset} 0.1333333 1.0000000 2.307692 4
## [11169] {network,
## dataset,
## process} => {architectur} 0.1333333 1.0000000 3.750000 4
## [11170] {network,
## architectur,
## dataset} => {process} 0.1333333 0.8000000 4.000000 4
## [11171] {architectur,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [11172] {network,
## architectur,
## process} => {propos} 0.1000000 0.7500000 1.500000 3
## [11173] {network,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11174] {model,
## architectur,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11175] {featur,
## architectur,
## process} => {model} 0.1000000 0.7500000 1.406250 3
## [11176] {featur,
## model,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11177] {featur,
## model,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [11178] {featur,
## architectur,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [11179] {network,
## architectur,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11180] {featur,
## network,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11181] {classif,
## dataset,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11182] {classif,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11183] {dataset,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [11184] {classif,
## dataset,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [11185] {classif,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11186] {classif,
## network,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11187] {network,
## dataset,
## process} => {classif} 0.1000000 0.7500000 2.812500 3
## [11188] {classif,
## network,
## dataset} => {process} 0.1000000 1.0000000 5.000000 3
## [11189] {classif,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [11190] {classif,
## network,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11191] {network,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [11192] {classif,
## network,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11193] {model,
## process,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [11194] {featur,
## process,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [11195] {featur,
## model,
## process} => {recognit} 0.1000000 0.7500000 2.500000 3
## [11196] {featur,
## model,
## recognit} => {process} 0.1000000 0.7500000 3.750000 3
## [11197] {improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11198] {algorithm,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11199] {algorithm,
## neural,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11200] {algorithm,
## improv,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11201] {improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11202] {improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11203] {neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11204] {improv,
## neural,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11205] {improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11206] {dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11207] {dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11208] {improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11209] {network,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11210] {network,
## neural,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11211] {network,
## improv,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11212] {algorithm,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11213] {improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11214] {algorithm,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11215] {algorithm,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11216] {algorithm,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11217] {dataset,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11218] {algorithm,
## dataset,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11219] {algorithm,
## dataset,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [11220] {algorithm,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11221] {network,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11222] {network,
## algorithm,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11223] {network,
## algorithm,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [11224] {improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11225] {dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11226] {dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11227] {improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11228] {network,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11229] {network,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11230] {network,
## improv,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11231] {dataset,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11232] {network,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11233] {network,
## dataset,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11234] {network,
## dataset,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [11235] {algorithm,
## process,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [11236] {featur,
## process,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11237] {featur,
## algorithm,
## process} => {result} 0.1000000 0.7500000 2.250000 3
## [11238] {featur,
## algorithm,
## result} => {process} 0.1000000 1.0000000 5.000000 3
## [11239] {algorithm,
## neural,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [11240] {approach,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11241] {approach,
## algorithm,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11242] {approach,
## algorithm,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11243] {algorithm,
## neural,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11244] {neural,
## process,
## work} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11245] {algorithm,
## process,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [11246] {algorithm,
## neural,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [11247] {algorithm,
## neural,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11248] {neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11249] {algorithm,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11250] {algorithm,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11251] {algorithm,
## neural,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11252] {dataset,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11253] {algorithm,
## dataset,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11254] {algorithm,
## dataset,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11255] {algorithm,
## neural,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11256] {show,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11257] {show,
## algorithm,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11258] {show,
## algorithm,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [11259] {algorithm,
## neural,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11260] {featur,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11261] {featur,
## algorithm,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11262] {featur,
## algorithm,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [11263] {algorithm,
## neural,
## process} => {network} 0.1333333 1.0000000 1.578947 4
## [11264] {network,
## neural,
## process} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [11265] {network,
## algorithm,
## process} => {neural} 0.1333333 1.0000000 3.000000 4
## [11266] {network,
## algorithm,
## neural} => {process} 0.1333333 0.8000000 4.000000 4
## [11267] {approach,
## neural,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [11268] {show,
## neural,
## process} => {approach} 0.1000000 1.0000000 2.500000 3
## [11269] {approach,
## show,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11270] {approach,
## show,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11271] {approach,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11272] {network,
## neural,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [11273] {approach,
## network,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11274] {neural,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11275] {network,
## neural,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11276] {network,
## process,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [11277] {neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11278] {dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11279] {dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11280] {dataset,
## neural,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11281] {neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11282] {network,
## neural,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11283] {network,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11284] {network,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11285] {dataset,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11286] {network,
## neural,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11287] {network,
## dataset,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11288] {show,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11289] {network,
## neural,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11290] {network,
## show,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11291] {featur,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11292] {network,
## neural,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11293] {featur,
## network,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11294] {featur,
## network,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11295] {approach,
## algorithm,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [11296] {show,
## algorithm,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [11297] {approach,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11298] {approach,
## show,
## algorithm} => {process} 0.1000000 1.0000000 5.000000 3
## [11299] {approach,
## algorithm,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11300] {network,
## algorithm,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [11301] {approach,
## network,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11302] {approach,
## network,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [11303] {algorithm,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11304] {network,
## algorithm,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11305] {network,
## process,
## work} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11306] {network,
## algorithm,
## work} => {process} 0.1000000 0.7500000 3.750000 3
## [11307] {algorithm,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11308] {algorithm,
## dataset,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11309] {dataset,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11310] {algorithm,
## dataset,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11311] {algorithm,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11312] {network,
## algorithm,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11313] {network,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11314] {network,
## algorithm,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11315] {algorithm,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11316] {network,
## algorithm,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11317] {network,
## dataset,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11318] {network,
## algorithm,
## dataset} => {process} 0.1000000 0.7500000 3.750000 3
## [11319] {represent,
## algorithm,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11320] {featur,
## algorithm,
## process} => {represent} 0.1000000 0.7500000 1.500000 3
## [11321] {featur,
## represent,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11322] {featur,
## represent,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [11323] {show,
## algorithm,
## process} => {model} 0.1000000 0.7500000 1.406250 3
## [11324] {model,
## algorithm,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [11325] {model,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11326] {show,
## algorithm,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11327] {featur,
## algorithm,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11328] {featur,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11329] {featur,
## show,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [11330] {show,
## algorithm,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [11331] {network,
## algorithm,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11332] {network,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11333] {network,
## show,
## algorithm} => {process} 0.1000000 1.0000000 5.000000 3
## [11334] {model,
## algorithm,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11335] {featur,
## algorithm,
## process} => {model} 0.1000000 0.7500000 1.406250 3
## [11336] {featur,
## model,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11337] {featur,
## model,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [11338] {featur,
## algorithm,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [11339] {network,
## algorithm,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11340] {featur,
## network,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11341] {featur,
## network,
## algorithm} => {process} 0.1000000 1.0000000 5.000000 3
## [11342] {approach,
## show,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11343] {approach,
## network,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [11344] {network,
## show,
## process} => {approach} 0.1000000 1.0000000 2.500000 3
## [11345] {dataset,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11346] {network,
## process,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11347] {network,
## dataset,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11348] {featur,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11349] {network,
## process,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [11350] {featur,
## network,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11351] {dataset,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11352] {network,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11353] {network,
## dataset,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11354] {network,
## dataset,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11355] {dataset,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [11356] {network,
## dataset,
## process} => {propos} 0.1000000 0.7500000 1.500000 3
## [11357] {network,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11358] {featur,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11359] {network,
## dataset,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11360] {featur,
## network,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11361] {model,
## process,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11362] {featur,
## process,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [11363] {featur,
## model,
## process} => {learn} 0.1000000 0.7500000 1.730769 3
## [11364] {model,
## show,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11365] {featur,
## show,
## process} => {model} 0.1000000 1.0000000 1.875000 3
## [11366] {featur,
## model,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11367] {featur,
## model,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [11368] {model,
## network,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11369] {featur,
## network,
## process} => {model} 0.1000000 0.7500000 1.406250 3
## [11370] {approach,
## demonstr,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [11371] {perform,
## demonstr,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [11372] {approach,
## perform,
## demonstr} => {problem} 0.1000000 1.0000000 3.333333 3
## [11373] {approach,
## demonstr,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11374] {dataset,
## demonstr,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [11375] {approach,
## dataset,
## demonstr} => {problem} 0.1000000 1.0000000 3.333333 3
## [11376] {approach,
## dataset,
## problem} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [11377] {perform,
## demonstr,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11378] {dataset,
## demonstr,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [11379] {dataset,
## perform,
## demonstr} => {problem} 0.1000000 1.0000000 3.333333 3
## [11380] {dataset,
## perform,
## problem} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [11381] {improv,
## perform,
## demonstr} => {show} 0.1000000 1.0000000 1.875000 3
## [11382] {show,
## improv,
## demonstr} => {perform} 0.1000000 1.0000000 2.142857 3
## [11383] {show,
## perform,
## demonstr} => {improv} 0.1000000 1.0000000 3.333333 3
## [11384] {show,
## improv,
## perform} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [11385] {approach,
## perform,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11386] {approach,
## dataset,
## demonstr} => {perform} 0.1000000 1.0000000 2.142857 3
## [11387] {dataset,
## perform,
## demonstr} => {approach} 0.1000000 1.0000000 2.500000 3
## [11388] {perform,
## demonstr,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [11389] {propos,
## demonstr,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [11390] {perform,
## propos,
## demonstr} => {work} 0.1000000 1.0000000 2.500000 3
## [11391] {perform,
## propos,
## work} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [11392] {dataset,
## demonstr,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [11393] {propos,
## demonstr,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11394] {dataset,
## propos,
## demonstr} => {work} 0.1000000 1.0000000 2.500000 3
## [11395] {dataset,
## demonstr,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [11396] {model,
## dataset,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [11397] {model,
## demonstr,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11398] {dataset,
## demonstr,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11399] {featur,
## dataset,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [11400] {featur,
## demonstr,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11401] {model,
## dataset,
## demonstr} => {featur} 0.1000000 1.0000000 1.875000 3
## [11402] {featur,
## dataset,
## demonstr} => {model} 0.1000000 1.0000000 1.875000 3
## [11403] {featur,
## model,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11404] {model,
## demonstr,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11405] {featur,
## demonstr,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [11406] {featur,
## model,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [11407] {reduc,
## task,
## achiev} => {data} 0.1000000 1.0000000 2.307692 3
## [11408] {data,
## reduc,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [11409] {data,
## reduc,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11410] {data,
## task,
## achiev} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11411] {reduc,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11412] {network,
## reduc,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [11413] {network,
## reduc,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11414] {network,
## task,
## achiev} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11415] {data,
## reduc,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11416] {network,
## reduc,
## achiev} => {data} 0.1000000 1.0000000 2.307692 3
## [11417] {data,
## network,
## reduc} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11418] {data,
## network,
## achiev} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11419] {reduc,
## optim,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [11420] {reduc,
## improv,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [11421] {reduc,
## improv,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [11422] {improv,
## optim,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11423] {method,
## reduc,
## optim} => {show} 0.1000000 1.0000000 1.875000 3
## [11424] {reduc,
## show,
## optim} => {method} 0.1000000 1.0000000 2.727273 3
## [11425] {method,
## reduc,
## show} => {optim} 0.1000000 0.7500000 3.214286 3
## [11426] {method,
## show,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11427] {reduc,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11428] {network,
## reduc,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [11429] {network,
## reduc,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [11430] {network,
## optim,
## work} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11431] {paper,
## reduc,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [11432] {data,
## paper,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [11433] {data,
## reduc,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [11434] {data,
## paper,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11435] {paper,
## reduc,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [11436] {network,
## paper,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [11437] {network,
## reduc,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [11438] {network,
## paper,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11439] {data,
## paper,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [11440] {network,
## paper,
## reduc} => {data} 0.1000000 1.0000000 2.307692 3
## [11441] {data,
## network,
## reduc} => {paper} 0.1000000 0.7500000 2.250000 3
## [11442] {data,
## network,
## paper} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11443] {method,
## reduc,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [11444] {method,
## reduc,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [11445] {reduc,
## show,
## train} => {method} 0.1000000 0.7500000 2.045455 3
## [11446] {method,
## show,
## train} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11447] {approach,
## method,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11448] {method,
## reduc,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [11449] {approach,
## reduc,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [11450] {approach,
## method,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [11451] {method,
## network,
## reduc} => {approach} 0.1000000 1.0000000 2.500000 3
## [11452] {approach,
## network,
## reduc} => {method} 0.1000000 1.0000000 2.727273 3
## [11453] {method,
## reduc,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [11454] {method,
## model,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11455] {model,
## reduc,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [11456] {method,
## reduc,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [11457] {method,
## network,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11458] {network,
## reduc,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [11459] {method,
## network,
## show} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11460] {data,
## reduc,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [11461] {reduc,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [11462] {data,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [11463] {data,
## reduc,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [11464] {reduc,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [11465] {data,
## reduc,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [11466] {data,
## reduc,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [11467] {featur,
## reduc,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [11468] {data,
## featur,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [11469] {data,
## reduc,
## task} => {network} 0.1333333 1.0000000 1.578947 4
## [11470] {network,
## reduc,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [11471] {data,
## network,
## reduc} => {task} 0.1333333 1.0000000 2.727273 4
## [11472] {reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [11473] {reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [11474] {reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [11475] {reduc,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [11476] {featur,
## reduc,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [11477] {featur,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [11478] {reduc,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [11479] {network,
## reduc,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [11480] {network,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [11481] {reduc,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [11482] {featur,
## reduc,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [11483] {featur,
## reduc,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [11484] {reduc,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [11485] {network,
## reduc,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [11486] {network,
## reduc,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [11487] {network,
## show,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11488] {featur,
## reduc,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [11489] {network,
## reduc,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [11490] {featur,
## network,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [11491] {reduc,
## show,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [11492] {network,
## reduc,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [11493] {network,
## reduc,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [11494] {network,
## show,
## train} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11495] {approach,
## reduc,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [11496] {approach,
## network,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11497] {network,
## reduc,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [11498] {data,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [11499] {data,
## reduc,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [11500] {reduc,
## represent,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [11501] {data,
## reduc,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [11502] {data,
## featur,
## reduc} => {represent} 0.1000000 1.0000000 2.000000 3
## [11503] {featur,
## reduc,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [11504] {data,
## reduc,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [11505] {data,
## network,
## reduc} => {represent} 0.1000000 0.7500000 1.500000 3
## [11506] {network,
## reduc,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [11507] {data,
## reduc,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [11508] {data,
## featur,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11509] {featur,
## reduc,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [11510] {data,
## reduc,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [11511] {data,
## network,
## reduc} => {show} 0.1000000 0.7500000 1.406250 3
## [11512] {network,
## reduc,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [11513] {data,
## network,
## show} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11514] {data,
## featur,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [11515] {data,
## network,
## reduc} => {featur} 0.1000000 0.7500000 1.406250 3
## [11516] {featur,
## network,
## reduc} => {data} 0.1000000 1.0000000 2.307692 3
## [11517] {reduc,
## show,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [11518] {network,
## reduc,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [11519] {network,
## reduc,
## show} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11520] {reduc,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [11521] {featur,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [11522] {featur,
## reduc,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [11523] {reduc,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [11524] {network,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [11525] {network,
## reduc,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [11526] {featur,
## reduc,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [11527] {network,
## reduc,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [11528] {featur,
## network,
## reduc} => {represent} 0.1000000 1.0000000 2.000000 3
## [11529] {featur,
## reduc,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [11530] {network,
## reduc,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [11531] {featur,
## network,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11532] {paper,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11533] {network,
## paper,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [11534] {network,
## task,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [11535] {network,
## paper,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11536] {paper,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11537] {paper,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [11538] {train,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [11539] {paper,
## train,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11540] {paper,
## train,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [11541] {paper,
## represent,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [11542] {represent,
## train,
## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [11543] {paper,
## represent,
## train} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11544] {paper,
## train,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11545] {featur,
## paper,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [11546] {featur,
## train,
## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [11547] {paper,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [11548] {paper,
## represent,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11549] {represent,
## achiev,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [11550] {paper,
## represent,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11551] {paper,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11552] {featur,
## paper,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11553] {featur,
## achiev,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [11554] {featur,
## paper,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11555] {paper,
## represent,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11556] {featur,
## paper,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [11557] {featur,
## represent,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [11558] {featur,
## paper,
## represent} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11559] {achiev,
## dataset,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [11560] {featur,
## achiev,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11561] {featur,
## achiev,
## dataset} => {recognit} 0.1000000 0.7500000 2.500000 3
## [11562] {featur,
## dataset,
## recognit} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11563] {achiev,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11564] {achiev,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [11565] {achiev,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [11566] {achiev,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11567] {achiev,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [11568] {achiev,
## neural,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [11569] {improv,
## neural,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11570] {achiev,
## improv,
## neural} => {model} 0.1000000 1.0000000 1.875000 3
## [11571] {model,
## achiev,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [11572] {model,
## achiev,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [11573] {model,
## improv,
## neural} => {achiev} 0.1000000 1.0000000 4.285714 3
## [11574] {achiev,
## dataset,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [11575] {achiev,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11576] {achiev,
## dataset,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [11577] {dataset,
## improv,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11578] {achiev,
## dataset,
## improv} => {model} 0.1000000 1.0000000 1.875000 3
## [11579] {model,
## achiev,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11580] {model,
## achiev,
## dataset} => {improv} 0.1000000 0.7500000 2.500000 3
## [11581] {model,
## dataset,
## improv} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11582] {achiev,
## improv,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [11583] {model,
## achiev,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [11584] {model,
## achiev,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [11585] {model,
## improv,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [11586] {achiev,
## neural,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [11587] {approach,
## achiev,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [11588] {approach,
## achiev,
## neural} => {result} 0.1000000 1.0000000 3.000000 3
## [11589] {approach,
## neural,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11590] {achiev,
## neural,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [11591] {achiev,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [11592] {achiev,
## neural,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [11593] {neural,
## propos,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11594] {achiev,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11595] {network,
## achiev,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [11596] {network,
## achiev,
## neural} => {result} 0.1000000 1.0000000 3.000000 3
## [11597] {train,
## achiev,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11598] {network,
## achiev,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [11599] {network,
## train,
## achiev} => {result} 0.1000000 1.0000000 3.000000 3
## [11600] {approach,
## achiev,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [11601] {achiev,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [11602] {approach,
## achiev,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [11603] {approach,
## achiev,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11604] {network,
## achiev,
## result} => {approach} 0.1000000 0.7500000 1.875000 3
## [11605] {approach,
## network,
## achiev} => {result} 0.1000000 0.7500000 2.250000 3
## [11606] {approach,
## network,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11607] {achiev,
## dataset,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11608] {network,
## achiev,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11609] {network,
## achiev,
## dataset} => {result} 0.1000000 0.7500000 2.250000 3
## [11610] {achiev,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11611] {network,
## achiev,
## result} => {propos} 0.1000000 0.7500000 1.500000 3
## [11612] {network,
## achiev,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [11613] {network,
## propos,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11614] {featur,
## achiev,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11615] {network,
## achiev,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [11616] {featur,
## network,
## achiev} => {result} 0.1000000 0.7500000 2.250000 3
## [11617] {featur,
## network,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11618] {train,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11619] {achiev,
## neural,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [11620] {train,
## achiev,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [11621] {approach,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11622] {achiev,
## neural,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [11623] {approach,
## achiev,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [11624] {approach,
## achiev,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [11625] {network,
## achiev,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [11626] {approach,
## network,
## achiev} => {neural} 0.1000000 0.7500000 2.250000 3
## [11627] {achiev,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11628] {achiev,
## neural,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11629] {achiev,
## dataset,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [11630] {achiev,
## dataset,
## neural} => {model} 0.1000000 1.0000000 1.875000 3
## [11631] {model,
## achiev,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11632] {model,
## achiev,
## dataset} => {neural} 0.1000000 0.7500000 2.250000 3
## [11633] {model,
## dataset,
## neural} => {achiev} 0.1000000 1.0000000 4.285714 3
## [11634] {achiev,
## neural,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [11635] {model,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11636] {model,
## achiev,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [11637] {model,
## neural,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [11638] {achiev,
## neural,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [11639] {featur,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11640] {featur,
## achiev,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [11641] {featur,
## neural,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [11642] {achiev,
## neural,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [11643] {network,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11644] {network,
## achiev,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [11645] {approach,
## method,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11646] {method,
## achiev,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [11647] {approach,
## achiev,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [11648] {approach,
## method,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11649] {featur,
## method,
## achiev} => {approach} 0.1000000 1.0000000 2.500000 3
## [11650] {approach,
## featur,
## achiev} => {method} 0.1000000 1.0000000 2.727273 3
## [11651] {approach,
## method,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11652] {method,
## network,
## achiev} => {approach} 0.1000000 1.0000000 2.500000 3
## [11653] {approach,
## network,
## achiev} => {method} 0.1000000 0.7500000 2.045455 3
## [11654] {method,
## achiev,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [11655] {featur,
## method,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11656] {featur,
## achiev,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [11657] {method,
## achiev,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [11658] {method,
## network,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11659] {network,
## achiev,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [11660] {featur,
## method,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11661] {method,
## network,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11662] {featur,
## network,
## achiev} => {method} 0.1000000 0.7500000 2.045455 3
## [11663] {data,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11664] {network,
## task,
## achiev} => {data} 0.1000000 0.7500000 1.730769 3
## [11665] {data,
## network,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [11666] {task,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [11667] {represent,
## task,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11668] {represent,
## achiev,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [11669] {task,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11670] {featur,
## task,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11671] {featur,
## achiev,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [11672] {task,
## achiev,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [11673] {network,
## task,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [11674] {network,
## achiev,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [11675] {represent,
## task,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11676] {featur,
## task,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [11677] {featur,
## represent,
## achiev} => {task} 0.1000000 0.7500000 2.045455 3
## [11678] {represent,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11679] {network,
## task,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [11680] {network,
## represent,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [11681] {featur,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11682] {network,
## task,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [11683] {featur,
## network,
## achiev} => {task} 0.1000000 0.7500000 2.045455 3
## [11684] {train,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [11685] {represent,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11686] {represent,
## achiev,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [11687] {represent,
## train,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11688] {train,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11689] {featur,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11690] {featur,
## achiev,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [11691] {featur,
## train,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11692] {represent,
## train,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11693] {featur,
## train,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [11694] {featur,
## represent,
## achiev} => {train} 0.1000000 0.7500000 1.875000 3
## [11695] {approach,
## achiev,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [11696] {approach,
## achiev,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11697] {achiev,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [11698] {approach,
## achiev,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [11699] {approach,
## model,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11700] {model,
## achiev,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [11701] {approach,
## achiev,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [11702] {approach,
## network,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11703] {network,
## achiev,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [11704] {approach,
## achiev,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [11705] {approach,
## model,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11706] {model,
## achiev,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [11707] {approach,
## achiev,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [11708] {approach,
## featur,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11709] {featur,
## achiev,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [11710] {approach,
## achiev,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [11711] {approach,
## network,
## achiev} => {propos} 0.1333333 1.0000000 2.000000 4
## [11712] {network,
## achiev,
## propos} => {approach} 0.1333333 1.0000000 2.500000 4
## [11713] {approach,
## model,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11714] {approach,
## network,
## achiev} => {model} 0.1000000 0.7500000 1.406250 3
## [11715] {model,
## network,
## achiev} => {approach} 0.1000000 1.0000000 2.500000 3
## [11716] {approach,
## featur,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11717] {approach,
## network,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [11718] {featur,
## network,
## achiev} => {approach} 0.1000000 0.7500000 1.875000 3
## [11719] {data,
## achiev,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [11720] {data,
## achiev,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11721] {achiev,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [11722] {data,
## achiev,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [11723] {data,
## represent,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11724] {represent,
## achiev,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [11725] {data,
## achiev,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [11726] {data,
## featur,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11727] {featur,
## achiev,
## dataset} => {data} 0.1000000 0.7500000 1.730769 3
## [11728] {data,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [11729] {data,
## represent,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11730] {represent,
## achiev,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [11731] {data,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11732] {data,
## featur,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11733] {featur,
## achiev,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [11734] {data,
## represent,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11735] {data,
## featur,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [11736] {featur,
## represent,
## achiev} => {data} 0.1000000 0.7500000 1.730769 3
## [11737] {achiev,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [11738] {represent,
## achiev,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [11739] {represent,
## achiev,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11740] {achiev,
## dataset,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11741] {featur,
## achiev,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [11742] {featur,
## achiev,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11743] {represent,
## achiev,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [11744] {featur,
## achiev,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [11745] {featur,
## represent,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11746] {show,
## achiev,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [11747] {featur,
## achiev,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [11748] {featur,
## show,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11749] {featur,
## show,
## dataset} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11750] {show,
## achiev,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [11751] {network,
## achiev,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [11752] {network,
## show,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11753] {achiev,
## dataset,
## propos} => {model} 0.1333333 1.0000000 1.875000 4
## [11754] {model,
## achiev,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [11755] {model,
## achiev,
## propos} => {dataset} 0.1333333 1.0000000 2.307692 4
## [11756] {achiev,
## dataset,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [11757] {featur,
## achiev,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [11758] {featur,
## achiev,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11759] {achiev,
## dataset,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [11760] {network,
## achiev,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [11761] {network,
## achiev,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11762] {model,
## achiev,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [11763] {featur,
## achiev,
## dataset} => {model} 0.1000000 0.7500000 1.406250 3
## [11764] {featur,
## model,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11765] {model,
## achiev,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [11766] {network,
## achiev,
## dataset} => {model} 0.1000000 0.7500000 1.406250 3
## [11767] {model,
## network,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11768] {model,
## network,
## dataset} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11769] {featur,
## achiev,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [11770] {network,
## achiev,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [11771] {featur,
## network,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11772] {represent,
## achiev,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [11773] {achiev,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [11774] {represent,
## achiev,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [11775] {represent,
## achiev,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [11776] {featur,
## achiev,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [11777] {featur,
## represent,
## achiev} => {learn} 0.1333333 1.0000000 2.307692 4
## [11778] {represent,
## achiev,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [11779] {network,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [11780] {network,
## represent,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11781] {achiev,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11782] {featur,
## achiev,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [11783] {featur,
## achiev,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [11784] {featur,
## achiev,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [11785] {network,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11786] {featur,
## network,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [11787] {represent,
## achiev,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [11788] {featur,
## represent,
## achiev} => {propos} 0.1000000 0.7500000 1.500000 3
## [11789] {featur,
## achiev,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [11790] {featur,
## represent,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [11791] {network,
## represent,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11792] {featur,
## network,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [11793] {featur,
## show,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11794] {network,
## show,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11795] {featur,
## network,
## achiev} => {show} 0.1000000 0.7500000 1.406250 3
## [11796] {model,
## achiev,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [11797] {featur,
## achiev,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [11798] {featur,
## model,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11799] {model,
## achiev,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [11800] {network,
## achiev,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [11801] {model,
## network,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11802] {model,
## network,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11803] {featur,
## achiev,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [11804] {network,
## achiev,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [11805] {featur,
## network,
## achiev} => {propos} 0.1000000 0.7500000 1.500000 3
## [11806] {algorithm,
## optim,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [11807] {train,
## optim,
## signific} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11808] {train,
## algorithm,
## optim} => {signific} 0.1000000 0.7500000 2.812500 3
## [11809] {train,
## algorithm,
## signific} => {optim} 0.1000000 1.0000000 4.285714 3
## [11810] {object,
## optim,
## problem} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11811] {algorithm,
## object,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [11812] {algorithm,
## optim,
## problem} => {object} 0.1000000 0.7500000 2.812500 3
## [11813] {algorithm,
## object,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [11814] {task,
## object,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11815] {data,
## object,
## optim} => {task} 0.1000000 1.0000000 2.727273 3
## [11816] {data,
## task,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [11817] {data,
## task,
## object} => {optim} 0.1000000 0.7500000 3.214286 3
## [11818] {task,
## object,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [11819] {object,
## propos,
## optim} => {task} 0.1000000 1.0000000 2.727273 3
## [11820] {task,
## propos,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [11821] {task,
## object,
## propos} => {optim} 0.1000000 0.7500000 3.214286 3
## [11822] {task,
## object,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [11823] {featur,
## object,
## optim} => {task} 0.1000000 1.0000000 2.727273 3
## [11824] {featur,
## task,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [11825] {featur,
## task,
## object} => {optim} 0.1000000 0.7500000 3.214286 3
## [11826] {data,
## object,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [11827] {object,
## propos,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11828] {data,
## propos,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [11829] {data,
## object,
## propos} => {optim} 0.1000000 0.7500000 3.214286 3
## [11830] {data,
## object,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [11831] {featur,
## object,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11832] {data,
## featur,
## optim} => {object} 0.1000000 0.7500000 2.812500 3
## [11833] {data,
## featur,
## object} => {optim} 0.1000000 0.7500000 3.214286 3
## [11834] {show,
## object,
## optim} => {model} 0.1000000 1.0000000 1.875000 3
## [11835] {model,
## object,
## optim} => {show} 0.1000000 1.0000000 1.875000 3
## [11836] {model,
## show,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [11837] {model,
## show,
## object} => {optim} 0.1000000 0.7500000 3.214286 3
## [11838] {object,
## propos,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [11839] {featur,
## object,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [11840] {featur,
## propos,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [11841] {architectur,
## improv,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [11842] {architectur,
## optim,
## work} => {improv} 0.1000000 1.0000000 3.333333 3
## [11843] {improv,
## optim,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11844] {architectur,
## improv,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [11845] {architectur,
## improv,
## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [11846] {architectur,
## perform,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [11847] {improv,
## perform,
## optim} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11848] {architectur,
## improv,
## perform} => {optim} 0.1000000 0.7500000 3.214286 3
## [11849] {architectur,
## improv,
## optim} => {network} 0.1000000 1.0000000 1.578947 3
## [11850] {network,
## architectur,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [11851] {network,
## improv,
## optim} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11852] {network,
## architectur,
## improv} => {optim} 0.1000000 0.7500000 3.214286 3
## [11853] {architectur,
## optim,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [11854] {architectur,
## perform,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [11855] {perform,
## optim,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11856] {architectur,
## perform,
## work} => {optim} 0.1000000 0.7500000 3.214286 3
## [11857] {architectur,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11858] {network,
## architectur,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [11859] {network,
## optim,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11860] {architectur,
## perform,
## optim} => {network} 0.1000000 1.0000000 1.578947 3
## [11861] {network,
## architectur,
## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [11862] {network,
## perform,
## optim} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11863] {algorithm,
## optim,
## problem} => {train} 0.1000000 0.7500000 1.875000 3
## [11864] {train,
## optim,
## problem} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11865] {train,
## algorithm,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [11866] {train,
## algorithm,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [11867] {algorithm,
## optim,
## problem} => {perform} 0.1000000 0.7500000 1.607143 3
## [11868] {perform,
## optim,
## problem} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11869] {algorithm,
## perform,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [11870] {algorithm,
## perform,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [11871] {algorithm,
## optim,
## problem} => {show} 0.1000000 0.7500000 1.406250 3
## [11872] {show,
## optim,
## problem} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11873] {show,
## algorithm,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [11874] {show,
## algorithm,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [11875] {algorithm,
## optim,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [11876] {propos,
## optim,
## problem} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11877] {algorithm,
## propos,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [11878] {algorithm,
## propos,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [11879] {train,
## optim,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [11880] {show,
## optim,
## problem} => {train} 0.1000000 1.0000000 2.500000 3
## [11881] {show,
## train,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [11882] {show,
## train,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [11883] {perform,
## optim,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [11884] {propos,
## optim,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [11885] {perform,
## propos,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [11886] {algorithm,
## improv,
## optim} => {train} 0.1000000 1.0000000 2.500000 3
## [11887] {train,
## improv,
## optim} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11888] {train,
## algorithm,
## optim} => {improv} 0.1000000 0.7500000 2.500000 3
## [11889] {train,
## algorithm,
## improv} => {optim} 0.1000000 0.7500000 3.214286 3
## [11890] {improv,
## optim,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [11891] {improv,
## perform,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [11892] {perform,
## optim,
## work} => {improv} 0.1000000 1.0000000 3.333333 3
## [11893] {improv,
## perform,
## work} => {optim} 0.1000000 0.7500000 3.214286 3
## [11894] {improv,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11895] {network,
## improv,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [11896] {network,
## optim,
## work} => {improv} 0.1000000 0.7500000 2.500000 3
## [11897] {network,
## improv,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [11898] {improv,
## perform,
## optim} => {network} 0.1000000 1.0000000 1.578947 3
## [11899] {network,
## improv,
## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [11900] {network,
## perform,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [11901] {network,
## improv,
## perform} => {optim} 0.1000000 0.7500000 3.214286 3
## [11902] {train,
## algorithm,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [11903] {algorithm,
## perform,
## optim} => {train} 0.1000000 0.7500000 1.875000 3
## [11904] {train,
## perform,
## optim} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11905] {train,
## algorithm,
## perform} => {optim} 0.1000000 1.0000000 4.285714 3
## [11906] {train,
## algorithm,
## optim} => {show} 0.1000000 0.7500000 1.406250 3
## [11907] {show,
## algorithm,
## optim} => {train} 0.1000000 1.0000000 2.500000 3
## [11908] {show,
## train,
## optim} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11909] {show,
## train,
## algorithm} => {optim} 0.1000000 1.0000000 4.285714 3
## [11910] {algorithm,
## perform,
## optim} => {data} 0.1000000 0.7500000 1.730769 3
## [11911] {data,
## algorithm,
## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [11912] {data,
## perform,
## optim} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11913] {data,
## algorithm,
## perform} => {optim} 0.1000000 0.7500000 3.214286 3
## [11914] {algorithm,
## perform,
## optim} => {propos} 0.1000000 0.7500000 1.500000 3
## [11915] {algorithm,
## propos,
## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [11916] {perform,
## propos,
## optim} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11917] {algorithm,
## perform,
## propos} => {optim} 0.1000000 0.7500000 3.214286 3
## [11918] {algorithm,
## perform,
## optim} => {featur} 0.1000000 0.7500000 1.406250 3
## [11919] {featur,
## algorithm,
## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [11920] {featur,
## perform,
## optim} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11921] {featur,
## algorithm,
## perform} => {optim} 0.1000000 0.7500000 3.214286 3
## [11922] {data,
## algorithm,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [11923] {featur,
## algorithm,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11924] {data,
## featur,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11925] {data,
## featur,
## algorithm} => {optim} 0.1000000 0.7500000 3.214286 3
## [11926] {data,
## task,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [11927] {task,
## propos,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11928] {data,
## propos,
## optim} => {task} 0.1000000 1.0000000 2.727273 3
## [11929] {data,
## task,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [11930] {featur,
## task,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11931] {data,
## featur,
## optim} => {task} 0.1000000 0.7500000 2.045455 3
## [11932] {task,
## propos,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [11933] {featur,
## task,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [11934] {featur,
## propos,
## optim} => {task} 0.1000000 1.0000000 2.727273 3
## [11935] {perform,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11936] {network,
## optim,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [11937] {network,
## perform,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [11938] {network,
## perform,
## work} => {optim} 0.1000000 0.7500000 3.214286 3
## [11939] {dataset,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11940] {network,
## optim,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11941] {network,
## dataset,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [11942] {data,
## perform,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [11943] {featur,
## perform,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11944] {data,
## featur,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [11945] {data,
## represent,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [11946] {data,
## featur,
## optim} => {represent} 0.1000000 0.7500000 1.500000 3
## [11947] {featur,
## represent,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11948] {data,
## propos,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [11949] {data,
## featur,
## optim} => {propos} 0.1000000 0.7500000 1.500000 3
## [11950] {featur,
## propos,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11951] {train,
## object,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [11952] {show,
## object,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [11953] {show,
## train,
## signific} => {object} 0.1000000 0.7500000 2.812500 3
## [11954] {show,
## train,
## object} => {signific} 0.1000000 1.0000000 3.750000 3
## [11955] {featur,
## architectur,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [11956] {network,
## architectur,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [11957] {featur,
## network,
## signific} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11958] {show,
## problem,
## signific} => {model} 0.1000000 1.0000000 1.875000 3
## [11959] {model,
## problem,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [11960] {model,
## show,
## signific} => {problem} 0.1000000 1.0000000 3.333333 3
## [11961] {model,
## show,
## problem} => {signific} 0.1000000 0.7500000 2.812500 3
## [11962] {paper,
## task,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [11963] {paper,
## train,
## signific} => {task} 0.1000000 1.0000000 2.727273 3
## [11964] {task,
## train,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [11965] {paper,
## task,
## train} => {signific} 0.1000000 0.7500000 2.812500 3
## [11966] {paper,
## task,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [11967] {paper,
## learn,
## signific} => {task} 0.1000000 0.7500000 2.045455 3
## [11968] {task,
## learn,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [11969] {paper,
## task,
## learn} => {signific} 0.1000000 1.0000000 3.750000 3
## [11970] {paper,
## task,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [11971] {featur,
## paper,
## signific} => {task} 0.1000000 1.0000000 2.727273 3
## [11972] {featur,
## task,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [11973] {featur,
## paper,
## task} => {signific} 0.1000000 0.7500000 2.812500 3
## [11974] {paper,
## train,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [11975] {paper,
## learn,
## signific} => {train} 0.1000000 0.7500000 1.875000 3
## [11976] {train,
## learn,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [11977] {paper,
## train,
## learn} => {signific} 0.1000000 0.7500000 2.812500 3
## [11978] {paper,
## train,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [11979] {featur,
## paper,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [11980] {featur,
## train,
## signific} => {paper} 0.1000000 0.7500000 2.250000 3
## [11981] {paper,
## learn,
## signific} => {represent} 0.1000000 0.7500000 1.500000 3
## [11982] {paper,
## represent,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [11983] {represent,
## learn,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [11984] {paper,
## represent,
## learn} => {signific} 0.1000000 0.7500000 2.812500 3
## [11985] {paper,
## learn,
## signific} => {show} 0.1000000 0.7500000 1.406250 3
## [11986] {paper,
## show,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [11987] {show,
## learn,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [11988] {paper,
## show,
## learn} => {signific} 0.1000000 1.0000000 3.750000 3
## [11989] {paper,
## learn,
## signific} => {propos} 0.1000000 0.7500000 1.500000 3
## [11990] {paper,
## propos,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [11991] {propos,
## learn,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [11992] {paper,
## propos,
## learn} => {signific} 0.1000000 0.7500000 2.812500 3
## [11993] {paper,
## learn,
## signific} => {featur} 0.1000000 0.7500000 1.406250 3
## [11994] {featur,
## paper,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [11995] {featur,
## learn,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [11996] {featur,
## paper,
## learn} => {signific} 0.1000000 0.7500000 2.812500 3
## [11997] {train,
## result,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [11998] {represent,
## result,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [11999] {represent,
## train,
## signific} => {result} 0.1000000 0.7500000 2.250000 3
## [12000] {represent,
## train,
## result} => {signific} 0.1000000 0.7500000 2.812500 3
## [12001] {train,
## result,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [12002] {featur,
## result,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [12003] {featur,
## train,
## signific} => {result} 0.1000000 0.7500000 2.250000 3
## [12004] {featur,
## train,
## result} => {signific} 0.1000000 1.0000000 3.750000 3
## [12005] {train,
## result,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [12006] {network,
## result,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [12007] {network,
## train,
## signific} => {result} 0.1000000 1.0000000 3.000000 3
## [12008] {represent,
## result,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [12009] {featur,
## result,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [12010] {featur,
## represent,
## signific} => {result} 0.1000000 1.0000000 3.000000 3
## [12011] {represent,
## result,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [12012] {network,
## result,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [12013] {network,
## represent,
## signific} => {result} 0.1000000 1.0000000 3.000000 3
## [12014] {network,
## represent,
## result} => {signific} 0.1000000 0.7500000 2.812500 3
## [12015] {featur,
## result,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [12016] {network,
## result,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [12017] {featur,
## network,
## signific} => {result} 0.1000000 0.7500000 2.250000 3
## [12018] {featur,
## network,
## result} => {signific} 0.1000000 0.7500000 2.812500 3
## [12019] {task,
## train,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [12020] {task,
## learn,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [12021] {train,
## learn,
## signific} => {task} 0.1000000 1.0000000 2.727273 3
## [12022] {task,
## train,
## learn} => {signific} 0.1000000 0.7500000 2.812500 3
## [12023] {task,
## train,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [12024] {featur,
## task,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [12025] {featur,
## train,
## signific} => {task} 0.1000000 0.7500000 2.045455 3
## [12026] {featur,
## task,
## train} => {signific} 0.1000000 0.7500000 2.812500 3
## [12027] {task,
## learn,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [12028] {featur,
## task,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [12029] {featur,
## learn,
## signific} => {task} 0.1000000 1.0000000 2.727273 3
## [12030] {data,
## train,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [12031] {featur,
## train,
## signific} => {data} 0.1000000 0.7500000 1.730769 3
## [12032] {data,
## featur,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [12033] {train,
## learn,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [12034] {featur,
## train,
## signific} => {learn} 0.1000000 0.7500000 1.730769 3
## [12035] {featur,
## learn,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [12036] {featur,
## train,
## learn} => {signific} 0.1000000 0.7500000 2.812500 3
## [12037] {represent,
## train,
## signific} => {featur} 0.1000000 0.7500000 1.406250 3
## [12038] {featur,
## train,
## signific} => {represent} 0.1000000 0.7500000 1.500000 3
## [12039] {featur,
## represent,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [12040] {represent,
## train,
## signific} => {network} 0.1000000 0.7500000 1.184211 3
## [12041] {network,
## train,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [12042] {network,
## represent,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [12043] {featur,
## train,
## signific} => {network} 0.1000000 0.7500000 1.184211 3
## [12044] {network,
## train,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [12045] {featur,
## network,
## signific} => {train} 0.1000000 0.7500000 1.875000 3
## [12046] {featur,
## network,
## train} => {signific} 0.1000000 0.7500000 2.812500 3
## [12047] {show,
## perform,
## signific} => {propos} 0.1000000 1.0000000 2.000000 3
## [12048] {perform,
## propos,
## signific} => {show} 0.1000000 0.7500000 1.406250 3
## [12049] {show,
## propos,
## signific} => {perform} 0.1000000 1.0000000 2.142857 3
## [12050] {featur,
## dataset,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [12051] {network,
## dataset,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [12052] {featur,
## network,
## signific} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12053] {featur,
## represent,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [12054] {network,
## represent,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [12055] {featur,
## network,
## signific} => {represent} 0.1000000 0.7500000 1.500000 3
## [12056] {represent,
## object,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12057] {object,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12058] {represent,
## object,
## propos} => {success} 0.1000000 0.7500000 2.812500 3
## [12059] {represent,
## object,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [12060] {featur,
## object,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12061] {featur,
## represent,
## object} => {success} 0.1000000 1.0000000 3.750000 3
## [12062] {object,
## propos,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [12063] {featur,
## object,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12064] {featur,
## propos,
## success} => {object} 0.1000000 0.7500000 2.812500 3
## [12065] {approach,
## make,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12066] {make,
## represent,
## success} => {approach} 0.1000000 1.0000000 2.500000 3
## [12067] {approach,
## represent,
## success} => {make} 0.1000000 0.7500000 2.500000 3
## [12068] {approach,
## make,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12069] {make,
## propos,
## success} => {approach} 0.1000000 1.0000000 2.500000 3
## [12070] {approach,
## propos,
## success} => {make} 0.1000000 0.7500000 2.500000 3
## [12071] {approach,
## make,
## propos} => {success} 0.1000000 0.7500000 2.812500 3
## [12072] {make,
## represent,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12073] {make,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12074] {perform,
## problem,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12075] {represent,
## problem,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [12076] {represent,
## perform,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [12077] {represent,
## perform,
## problem} => {success} 0.1000000 0.7500000 2.812500 3
## [12078] {perform,
## problem,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12079] {propos,
## problem,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [12080] {perform,
## propos,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [12081] {represent,
## problem,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12082] {propos,
## problem,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12083] {represent,
## propos,
## problem} => {success} 0.1000000 0.7500000 2.812500 3
## [12084] {approach,
## method,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [12085] {method,
## learn,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [12086] {approach,
## learn,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [12087] {approach,
## method,
## learn} => {success} 0.1000000 0.7500000 2.812500 3
## [12088] {approach,
## method,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12089] {method,
## represent,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [12090] {approach,
## represent,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [12091] {approach,
## method,
## represent} => {success} 0.1000000 0.7500000 2.812500 3
## [12092] {approach,
## method,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12093] {method,
## propos,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [12094] {approach,
## propos,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [12095] {method,
## learn,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [12096] {method,
## represent,
## success} => {learn} 0.1333333 1.0000000 2.307692 4
## [12097] {represent,
## learn,
## success} => {method} 0.1333333 0.8000000 2.181818 4
## [12098] {method,
## represent,
## learn} => {success} 0.1333333 1.0000000 3.750000 4
## [12099] {method,
## learn,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [12100] {method,
## show,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [12101] {show,
## learn,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [12102] {method,
## show,
## learn} => {success} 0.1000000 0.7500000 2.812500 3
## [12103] {method,
## learn,
## success} => {propos} 0.1333333 1.0000000 2.000000 4
## [12104] {method,
## propos,
## success} => {learn} 0.1333333 1.0000000 2.307692 4
## [12105] {propos,
## learn,
## success} => {method} 0.1333333 1.0000000 2.727273 4
## [12106] {method,
## propos,
## learn} => {success} 0.1333333 1.0000000 3.750000 4
## [12107] {method,
## learn,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [12108] {featur,
## method,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [12109] {featur,
## learn,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [12110] {featur,
## method,
## learn} => {success} 0.1000000 0.7500000 2.812500 3
## [12111] {method,
## represent,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [12112] {method,
## show,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12113] {represent,
## show,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [12114] {method,
## represent,
## show} => {success} 0.1000000 0.7500000 2.812500 3
## [12115] {method,
## represent,
## success} => {propos} 0.1333333 1.0000000 2.000000 4
## [12116] {method,
## propos,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [12117] {represent,
## propos,
## success} => {method} 0.1333333 0.8000000 2.181818 4
## [12118] {method,
## represent,
## propos} => {success} 0.1333333 1.0000000 3.750000 4
## [12119] {method,
## represent,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [12120] {featur,
## method,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12121] {featur,
## method,
## represent} => {success} 0.1000000 0.7500000 2.812500 3
## [12122] {method,
## show,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12123] {method,
## propos,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [12124] {show,
## propos,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [12125] {method,
## propos,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [12126] {featur,
## method,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12127] {featur,
## propos,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [12128] {approach,
## task,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12129] {represent,
## task,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [12130] {approach,
## represent,
## success} => {task} 0.1000000 0.7500000 2.045455 3
## [12131] {approach,
## task,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12132] {task,
## propos,
## success} => {approach} 0.1000000 1.0000000 2.500000 3
## [12133] {approach,
## propos,
## success} => {task} 0.1000000 0.7500000 2.045455 3
## [12134] {approach,
## task,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [12135] {featur,
## task,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [12136] {approach,
## featur,
## success} => {task} 0.1000000 1.0000000 2.727273 3
## [12137] {data,
## task,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12138] {represent,
## task,
## success} => {data} 0.1000000 0.7500000 1.730769 3
## [12139] {data,
## represent,
## success} => {task} 0.1000000 1.0000000 2.727273 3
## [12140] {data,
## task,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [12141] {featur,
## task,
## success} => {data} 0.1000000 0.7500000 1.730769 3
## [12142] {data,
## featur,
## success} => {task} 0.1000000 1.0000000 2.727273 3
## [12143] {task,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12144] {represent,
## task,
## success} => {learn} 0.1000000 0.7500000 1.730769 3
## [12145] {task,
## learn,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [12146] {featur,
## task,
## success} => {learn} 0.1000000 0.7500000 1.730769 3
## [12147] {featur,
## learn,
## success} => {task} 0.1000000 0.7500000 2.045455 3
## [12148] {represent,
## task,
## success} => {propos} 0.1000000 0.7500000 1.500000 3
## [12149] {task,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12150] {represent,
## task,
## success} => {featur} 0.1333333 1.0000000 1.875000 4
## [12151] {featur,
## task,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [12152] {featur,
## represent,
## success} => {task} 0.1333333 0.8000000 2.181818 4
## [12153] {task,
## propos,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [12154] {featur,
## task,
## success} => {propos} 0.1000000 0.7500000 1.500000 3
## [12155] {featur,
## propos,
## success} => {task} 0.1000000 0.7500000 2.045455 3
## [12156] {approach,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12157] {approach,
## represent,
## success} => {learn} 0.1000000 0.7500000 1.730769 3
## [12158] {approach,
## learn,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12159] {approach,
## propos,
## success} => {learn} 0.1000000 0.7500000 1.730769 3
## [12160] {propos,
## learn,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [12161] {approach,
## represent,
## success} => {propos} 0.1333333 1.0000000 2.000000 4
## [12162] {approach,
## propos,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [12163] {represent,
## propos,
## success} => {approach} 0.1333333 0.8000000 2.000000 4
## [12164] {approach,
## represent,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [12165] {approach,
## featur,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12166] {approach,
## propos,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [12167] {approach,
## featur,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12168] {featur,
## propos,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [12169] {represent,
## perform,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [12170] {perform,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12171] {data,
## represent,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [12172] {data,
## featur,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12173] {represent,
## learn,
## success} => {show} 0.1333333 0.8000000 1.500000 4
## [12174] {show,
## learn,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [12175] {represent,
## show,
## success} => {learn} 0.1333333 1.0000000 2.307692 4
## [12176] {represent,
## learn,
## success} => {propos} 0.1333333 0.8000000 1.600000 4
## [12177] {propos,
## learn,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [12178] {represent,
## propos,
## success} => {learn} 0.1333333 0.8000000 1.846154 4
## [12179] {model,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12180] {model,
## represent,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [12181] {represent,
## learn,
## success} => {featur} 0.1333333 0.8000000 1.500000 4
## [12182] {featur,
## learn,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [12183] {featur,
## represent,
## success} => {learn} 0.1333333 0.8000000 1.846154 4
## [12184] {show,
## learn,
## success} => {propos} 0.1000000 0.7500000 1.500000 3
## [12185] {propos,
## learn,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [12186] {show,
## propos,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [12187] {show,
## learn,
## success} => {model} 0.1000000 0.7500000 1.406250 3
## [12188] {model,
## learn,
## success} => {show} 0.1000000 1.0000000 1.875000 3
## [12189] {model,
## show,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [12190] {show,
## learn,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [12191] {featur,
## learn,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [12192] {featur,
## show,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [12193] {propos,
## learn,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [12194] {featur,
## learn,
## success} => {propos} 0.1000000 0.7500000 1.500000 3
## [12195] {featur,
## propos,
## success} => {learn} 0.1000000 0.7500000 1.730769 3
## [12196] {represent,
## show,
## success} => {propos} 0.1000000 0.7500000 1.500000 3
## [12197] {show,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12198] {represent,
## show,
## success} => {model} 0.1000000 0.7500000 1.406250 3
## [12199] {model,
## represent,
## success} => {show} 0.1000000 1.0000000 1.875000 3
## [12200] {model,
## show,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12201] {represent,
## show,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [12202] {featur,
## show,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [12203] {represent,
## propos,
## success} => {featur} 0.1333333 0.8000000 1.500000 4
## [12204] {featur,
## represent,
## success} => {propos} 0.1333333 0.8000000 1.600000 4
## [12205] {featur,
## propos,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [12206] {classif,
## architectur,
## experi} => {neural} 0.1000000 0.7500000 2.250000 3
## [12207] {architectur,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [12208] {classif,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12209] {classif,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [12210] {classif,
## architectur,
## experi} => {method} 0.1000000 0.7500000 2.045455 3
## [12211] {method,
## architectur,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [12212] {classif,
## method,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12213] {classif,
## method,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [12214] {classif,
## architectur,
## experi} => {approach} 0.1000000 0.7500000 1.875000 3
## [12215] {approach,
## architectur,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [12216] {approach,
## classif,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12217] {approach,
## classif,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [12218] {classif,
## architectur,
## experi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12219] {architectur,
## dataset,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [12220] {classif,
## dataset,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12221] {classif,
## architectur,
## dataset} => {experi} 0.1000000 1.0000000 3.750000 3
## [12222] {classif,
## architectur,
## experi} => {propos} 0.1333333 1.0000000 2.000000 4
## [12223] {architectur,
## experi,
## propos} => {classif} 0.1333333 1.0000000 3.750000 4
## [12224] {classif,
## experi,
## propos} => {architectur} 0.1333333 1.0000000 3.750000 4
## [12225] {classif,
## architectur,
## propos} => {experi} 0.1333333 1.0000000 3.750000 4
## [12226] {classif,
## architectur,
## experi} => {featur} 0.1000000 0.7500000 1.406250 3
## [12227] {featur,
## architectur,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [12228] {classif,
## featur,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12229] {classif,
## featur,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [12230] {classif,
## architectur,
## experi} => {network} 0.1333333 1.0000000 1.578947 4
## [12231] {network,
## architectur,
## experi} => {classif} 0.1333333 1.0000000 3.750000 4
## [12232] {classif,
## network,
## experi} => {architectur} 0.1333333 1.0000000 3.750000 4
## [12233] {classif,
## network,
## architectur} => {experi} 0.1333333 1.0000000 3.750000 4
## [12234] {architectur,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [12235] {method,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [12236] {method,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12237] {method,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [12238] {architectur,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [12239] {approach,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [12240] {approach,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12241] {approach,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [12242] {architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [12243] {architectur,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [12244] {experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12245] {architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [12246] {architectur,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12247] {network,
## architectur,
## experi} => {neural} 0.1000000 0.7500000 2.250000 3
## [12248] {network,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12249] {network,
## architectur,
## neural} => {experi} 0.1000000 0.7500000 2.812500 3
## [12250] {method,
## architectur,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [12251] {approach,
## architectur,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [12252] {approach,
## method,
## experi} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12253] {approach,
## method,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [12254] {method,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12255] {architectur,
## experi,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [12256] {method,
## experi,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12257] {method,
## architectur,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [12258] {method,
## architectur,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [12259] {network,
## architectur,
## experi} => {method} 0.1000000 0.7500000 2.045455 3
## [12260] {method,
## network,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12261] {method,
## network,
## architectur} => {experi} 0.1000000 0.7500000 2.812500 3
## [12262] {approach,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12263] {architectur,
## experi,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [12264] {approach,
## experi,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12265] {approach,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [12266] {approach,
## architectur,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [12267] {network,
## architectur,
## experi} => {approach} 0.1000000 0.7500000 1.875000 3
## [12268] {approach,
## network,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12269] {approach,
## network,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [12270] {architectur,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12271] {architectur,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12272] {dataset,
## experi,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12273] {architectur,
## dataset,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [12274] {architectur,
## dataset,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [12275] {network,
## architectur,
## experi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12276] {network,
## dataset,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12277] {architectur,
## experi,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [12278] {featur,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12279] {featur,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12280] {featur,
## architectur,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [12281] {architectur,
## experi,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [12282] {network,
## architectur,
## experi} => {propos} 0.1333333 1.0000000 2.000000 4
## [12283] {network,
## experi,
## propos} => {architectur} 0.1333333 1.0000000 3.750000 4
## [12284] {network,
## architectur,
## propos} => {experi} 0.1333333 0.8000000 3.000000 4
## [12285] {featur,
## architectur,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [12286] {network,
## architectur,
## experi} => {featur} 0.1000000 0.7500000 1.406250 3
## [12287] {featur,
## network,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12288] {make,
## paper,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [12289] {make,
## method,
## experi} => {paper} 0.1000000 1.0000000 3.000000 3
## [12290] {method,
## paper,
## experi} => {make} 0.1000000 1.0000000 3.333333 3
## [12291] {make,
## method,
## paper} => {experi} 0.1000000 0.7500000 2.812500 3
## [12292] {classif,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [12293] {classif,
## method,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [12294] {method,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [12295] {classif,
## method,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [12296] {classif,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [12297] {approach,
## classif,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [12298] {approach,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [12299] {approach,
## classif,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [12300] {classif,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [12301] {classif,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [12302] {experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [12303] {classif,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [12304] {classif,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12305] {classif,
## network,
## experi} => {neural} 0.1000000 0.7500000 2.250000 3
## [12306] {network,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [12307] {classif,
## network,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [12308] {classif,
## method,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [12309] {approach,
## classif,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [12310] {approach,
## method,
## experi} => {classif} 0.1000000 0.7500000 2.812500 3
## [12311] {classif,
## method,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12312] {classif,
## experi,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [12313] {method,
## experi,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [12314] {classif,
## method,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [12315] {classif,
## method,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [12316] {classif,
## network,
## experi} => {method} 0.1000000 0.7500000 2.045455 3
## [12317] {method,
## network,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [12318] {classif,
## method,
## network} => {experi} 0.1000000 0.7500000 2.812500 3
## [12319] {approach,
## classif,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12320] {classif,
## experi,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [12321] {approach,
## experi,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [12322] {approach,
## classif,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [12323] {approach,
## classif,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [12324] {classif,
## network,
## experi} => {approach} 0.1000000 0.7500000 1.875000 3
## [12325] {approach,
## network,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [12326] {approach,
## classif,
## network} => {experi} 0.1000000 0.7500000 2.812500 3
## [12327] {classif,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12328] {classif,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12329] {dataset,
## experi,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [12330] {classif,
## dataset,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [12331] {classif,
## dataset,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [12332] {classif,
## network,
## experi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12333] {network,
## dataset,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [12334] {classif,
## network,
## dataset} => {experi} 0.1000000 1.0000000 3.750000 3
## [12335] {classif,
## experi,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [12336] {classif,
## featur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12337] {featur,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [12338] {classif,
## experi,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [12339] {classif,
## network,
## experi} => {propos} 0.1333333 1.0000000 2.000000 4
## [12340] {network,
## experi,
## propos} => {classif} 0.1333333 1.0000000 3.750000 4
## [12341] {classif,
## network,
## propos} => {experi} 0.1333333 1.0000000 3.750000 4
## [12342] {classif,
## featur,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [12343] {classif,
## network,
## experi} => {featur} 0.1000000 0.7500000 1.406250 3
## [12344] {featur,
## network,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [12345] {classif,
## featur,
## network} => {experi} 0.1000000 0.7500000 2.812500 3
## [12346] {method,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [12347] {approach,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [12348] {approach,
## method,
## experi} => {neural} 0.1000000 0.7500000 2.250000 3
## [12349] {approach,
## method,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [12350] {method,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [12351] {experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [12352] {method,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [12353] {method,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [12354] {method,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12355] {network,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [12356] {method,
## network,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [12357] {method,
## network,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [12358] {approach,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [12359] {experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [12360] {approach,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [12361] {approach,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12362] {network,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [12363] {approach,
## network,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [12364] {experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [12365] {network,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [12366] {network,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [12367] {method,
## algorithm,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [12368] {method,
## experi,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12369] {algorithm,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [12370] {method,
## algorithm,
## perform} => {experi} 0.1000000 1.0000000 3.750000 3
## [12371] {method,
## algorithm,
## experi} => {show} 0.1000000 1.0000000 1.875000 3
## [12372] {method,
## show,
## experi} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12373] {show,
## algorithm,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [12374] {method,
## show,
## algorithm} => {experi} 0.1000000 0.7500000 2.812500 3
## [12375] {approach,
## method,
## experi} => {perform} 0.1000000 0.7500000 1.607143 3
## [12376] {method,
## experi,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [12377] {approach,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [12378] {approach,
## method,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [12379] {approach,
## method,
## experi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12380] {method,
## dataset,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [12381] {approach,
## dataset,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [12382] {approach,
## method,
## experi} => {show} 0.1000000 0.7500000 1.406250 3
## [12383] {method,
## show,
## experi} => {approach} 0.1000000 0.7500000 1.875000 3
## [12384] {approach,
## show,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [12385] {approach,
## method,
## experi} => {propos} 0.1333333 1.0000000 2.000000 4
## [12386] {method,
## experi,
## propos} => {approach} 0.1333333 1.0000000 2.500000 4
## [12387] {approach,
## experi,
## propos} => {method} 0.1333333 1.0000000 2.727273 4
## [12388] {approach,
## method,
## propos} => {experi} 0.1333333 0.8000000 3.000000 4
## [12389] {approach,
## method,
## experi} => {network} 0.1000000 0.7500000 1.184211 3
## [12390] {method,
## network,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [12391] {approach,
## network,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [12392] {method,
## experi,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12393] {method,
## dataset,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [12394] {dataset,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [12395] {method,
## experi,
## perform} => {show} 0.1333333 1.0000000 1.875000 4
## [12396] {method,
## show,
## experi} => {perform} 0.1333333 1.0000000 2.142857 4
## [12397] {show,
## experi,
## perform} => {method} 0.1333333 1.0000000 2.727273 4
## [12398] {method,
## experi,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [12399] {method,
## experi,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [12400] {experi,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [12401] {method,
## experi,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [12402] {method,
## model,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [12403] {model,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [12404] {method,
## model,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [12405] {method,
## dataset,
## experi} => {show} 0.1000000 1.0000000 1.875000 3
## [12406] {method,
## show,
## experi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12407] {show,
## dataset,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [12408] {method,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12409] {method,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12410] {dataset,
## experi,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [12411] {method,
## show,
## experi} => {propos} 0.1000000 0.7500000 1.500000 3
## [12412] {method,
## experi,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [12413] {show,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [12414] {method,
## show,
## experi} => {model} 0.1000000 0.7500000 1.406250 3
## [12415] {method,
## model,
## experi} => {show} 0.1000000 1.0000000 1.875000 3
## [12416] {model,
## show,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [12417] {method,
## experi,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [12418] {method,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12419] {network,
## experi,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [12420] {algorithm,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [12421] {show,
## algorithm,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [12422] {show,
## experi,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12423] {show,
## algorithm,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [12424] {approach,
## experi,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12425] {approach,
## dataset,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [12426] {dataset,
## experi,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [12427] {approach,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [12428] {approach,
## show,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [12429] {show,
## experi,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [12430] {approach,
## show,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [12431] {approach,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [12432] {approach,
## experi,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [12433] {experi,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [12434] {approach,
## dataset,
## experi} => {show} 0.1000000 1.0000000 1.875000 3
## [12435] {approach,
## show,
## experi} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12436] {show,
## dataset,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [12437] {approach,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12438] {approach,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12439] {dataset,
## experi,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [12440] {approach,
## show,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12441] {approach,
## experi,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [12442] {show,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [12443] {approach,
## experi,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [12444] {approach,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12445] {network,
## experi,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [12446] {dataset,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [12447] {show,
## experi,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12448] {show,
## dataset,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [12449] {show,
## dataset,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [12450] {dataset,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [12451] {experi,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12452] {dataset,
## experi,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [12453] {show,
## experi,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [12454] {experi,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [12455] {show,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [12456] {show,
## experi,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [12457] {model,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [12458] {model,
## show,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [12459] {show,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12460] {dataset,
## experi,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [12461] {show,
## experi,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12462] {dataset,
## experi,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [12463] {model,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12464] {model,
## experi,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12465] {dataset,
## experi,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [12466] {network,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12467] {network,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12468] {featur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [12469] {network,
## experi,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [12470] {featur,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [12471] {classif,
## object,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [12472] {classif,
## object,
## propos} => {recognit} 0.1000000 0.7500000 2.500000 3
## [12473] {object,
## propos,
## recognit} => {classif} 0.1000000 0.7500000 2.812500 3
## [12474] {classif,
## propos,
## recognit} => {object} 0.1000000 1.0000000 3.750000 3
## [12475] {classif,
## object,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [12476] {classif,
## featur,
## object} => {recognit} 0.1000000 0.7500000 2.500000 3
## [12477] {featur,
## object,
## recognit} => {classif} 0.1000000 1.0000000 3.750000 3
## [12478] {classif,
## featur,
## recognit} => {object} 0.1000000 1.0000000 3.750000 3
## [12479] {classif,
## object,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [12480] {classif,
## show,
## object} => {perform} 0.1000000 1.0000000 2.142857 3
## [12481] {show,
## object,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [12482] {classif,
## object,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [12483] {classif,
## object,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [12484] {classif,
## perform,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [12485] {classif,
## object,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [12486] {classif,
## featur,
## object} => {perform} 0.1000000 0.7500000 1.607143 3
## [12487] {featur,
## object,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [12488] {classif,
## featur,
## perform} => {object} 0.1000000 0.7500000 2.812500 3
## [12489] {classif,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [12490] {classif,
## object,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [12491] {object,
## propos,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [12492] {classif,
## propos,
## learn} => {object} 0.1000000 0.7500000 2.812500 3
## [12493] {classif,
## object,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [12494] {classif,
## featur,
## object} => {learn} 0.1000000 0.7500000 1.730769 3
## [12495] {featur,
## object,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [12496] {classif,
## show,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12497] {classif,
## object,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [12498] {classif,
## show,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [12499] {classif,
## show,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [12500] {classif,
## featur,
## object} => {show} 0.1000000 0.7500000 1.406250 3
## [12501] {featur,
## show,
## object} => {classif} 0.1000000 0.7500000 2.812500 3
## [12502] {classif,
## object,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [12503] {classif,
## model,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12504] {model,
## object,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [12505] {classif,
## model,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [12506] {classif,
## object,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [12507] {classif,
## featur,
## object} => {propos} 0.1333333 1.0000000 2.000000 4
## [12508] {classif,
## featur,
## propos} => {object} 0.1333333 0.8000000 3.000000 4
## [12509] {classif,
## model,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [12510] {classif,
## featur,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [12511] {featur,
## model,
## object} => {classif} 0.1000000 0.7500000 2.812500 3
## [12512] {object,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [12513] {object,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [12514] {object,
## perform,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [12515] {featur,
## object,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [12516] {featur,
## object,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [12517] {object,
## propos,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [12518] {featur,
## object,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [12519] {featur,
## propos,
## problem} => {object} 0.1000000 0.7500000 2.812500 3
## [12520] {object,
## perform,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [12521] {show,
## object,
## recognit} => {perform} 0.1000000 1.0000000 2.142857 3
## [12522] {show,
## object,
## perform} => {recognit} 0.1000000 0.7500000 2.500000 3
## [12523] {show,
## perform,
## recognit} => {object} 0.1000000 1.0000000 3.750000 3
## [12524] {object,
## perform,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [12525] {object,
## propos,
## recognit} => {perform} 0.1000000 0.7500000 1.607143 3
## [12526] {perform,
## propos,
## recognit} => {object} 0.1000000 0.7500000 2.812500 3
## [12527] {show,
## object,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [12528] {object,
## propos,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [12529] {show,
## propos,
## recognit} => {object} 0.1000000 0.7500000 2.812500 3
## [12530] {object,
## propos,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [12531] {featur,
## object,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [12532] {featur,
## propos,
## recognit} => {object} 0.1000000 0.7500000 2.812500 3
## [12533] {method,
## show,
## object} => {propos} 0.1000000 0.7500000 1.500000 3
## [12534] {method,
## object,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [12535] {method,
## show,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [12536] {method,
## model,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [12537] {model,
## show,
## object} => {method} 0.1000000 0.7500000 2.045455 3
## [12538] {method,
## show,
## object} => {featur} 0.1000000 0.7500000 1.406250 3
## [12539] {featur,
## method,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [12540] {featur,
## show,
## object} => {method} 0.1000000 0.7500000 2.045455 3
## [12541] {method,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [12542] {featur,
## method,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12543] {algorithm,
## object,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [12544] {algorithm,
## object,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [12545] {algorithm,
## perform,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [12546] {algorithm,
## object,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [12547] {featur,
## algorithm,
## object} => {perform} 0.1000000 1.0000000 2.142857 3
## [12548] {featur,
## object,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12549] {featur,
## algorithm,
## perform} => {object} 0.1000000 0.7500000 2.812500 3
## [12550] {show,
## algorithm,
## object} => {model} 0.1000000 1.0000000 1.875000 3
## [12551] {model,
## algorithm,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [12552] {model,
## show,
## object} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12553] {algorithm,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [12554] {featur,
## algorithm,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12555] {featur,
## algorithm,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [12556] {data,
## task,
## object} => {learn} 0.1000000 0.7500000 1.730769 3
## [12557] {task,
## object,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [12558] {data,
## object,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [12559] {data,
## task,
## object} => {propos} 0.1333333 1.0000000 2.000000 4
## [12560] {task,
## object,
## propos} => {data} 0.1333333 1.0000000 2.307692 4
## [12561] {data,
## object,
## propos} => {task} 0.1333333 1.0000000 2.727273 4
## [12562] {data,
## task,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [12563] {model,
## task,
## object} => {data} 0.1000000 1.0000000 2.307692 3
## [12564] {data,
## model,
## object} => {task} 0.1000000 1.0000000 2.727273 3
## [12565] {data,
## task,
## object} => {featur} 0.1333333 1.0000000 1.875000 4
## [12566] {featur,
## task,
## object} => {data} 0.1333333 1.0000000 2.307692 4
## [12567] {data,
## featur,
## object} => {task} 0.1333333 1.0000000 2.727273 4
## [12568] {task,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [12569] {task,
## object,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [12570] {object,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [12571] {task,
## object,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [12572] {model,
## task,
## object} => {learn} 0.1000000 1.0000000 2.307692 3
## [12573] {model,
## object,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [12574] {task,
## object,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [12575] {featur,
## task,
## object} => {learn} 0.1000000 0.7500000 1.730769 3
## [12576] {featur,
## object,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [12577] {task,
## object,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [12578] {model,
## task,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12579] {model,
## object,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [12580] {model,
## task,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [12581] {task,
## object,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [12582] {featur,
## task,
## object} => {propos} 0.1333333 1.0000000 2.000000 4
## [12583] {model,
## task,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [12584] {featur,
## task,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [12585] {featur,
## model,
## object} => {task} 0.1000000 0.7500000 2.045455 3
## [12586] {approach,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [12587] {approach,
## featur,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12588] {represent,
## object,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [12589] {represent,
## object,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [12590] {show,
## object,
## perform} => {propos} 0.1333333 1.0000000 2.000000 4
## [12591] {object,
## perform,
## propos} => {show} 0.1333333 0.8000000 1.500000 4
## [12592] {show,
## object,
## propos} => {perform} 0.1333333 0.8000000 1.714286 4
## [12593] {show,
## object,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [12594] {featur,
## object,
## perform} => {show} 0.1000000 0.7500000 1.406250 3
## [12595] {featur,
## show,
## object} => {perform} 0.1000000 0.7500000 1.607143 3
## [12596] {featur,
## show,
## perform} => {object} 0.1000000 0.7500000 2.812500 3
## [12597] {object,
## perform,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [12598] {featur,
## object,
## perform} => {propos} 0.1333333 1.0000000 2.000000 4
## [12599] {data,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [12600] {data,
## object,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [12601] {object,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [12602] {data,
## object,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [12603] {data,
## model,
## object} => {learn} 0.1000000 1.0000000 2.307692 3
## [12604] {model,
## object,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [12605] {data,
## object,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [12606] {data,
## featur,
## object} => {learn} 0.1000000 0.7500000 1.730769 3
## [12607] {featur,
## object,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [12608] {data,
## object,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [12609] {data,
## model,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12610] {model,
## object,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [12611] {data,
## object,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [12612] {data,
## featur,
## object} => {propos} 0.1333333 1.0000000 2.000000 4
## [12613] {data,
## model,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [12614] {data,
## featur,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [12615] {featur,
## model,
## object} => {data} 0.1000000 0.7500000 1.730769 3
## [12616] {dataset,
## object,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [12617] {model,
## dataset,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12618] {model,
## object,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12619] {dataset,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [12620] {featur,
## dataset,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12621] {dataset,
## object,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [12622] {network,
## dataset,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12623] {network,
## object,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12624] {model,
## dataset,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [12625] {featur,
## dataset,
## object} => {model} 0.1000000 1.0000000 1.875000 3
## [12626] {featur,
## model,
## object} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12627] {model,
## dataset,
## object} => {network} 0.1000000 1.0000000 1.578947 3
## [12628] {network,
## dataset,
## object} => {model} 0.1000000 1.0000000 1.875000 3
## [12629] {model,
## network,
## object} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12630] {model,
## network,
## dataset} => {object} 0.1000000 0.7500000 2.812500 3
## [12631] {featur,
## dataset,
## object} => {network} 0.1000000 1.0000000 1.578947 3
## [12632] {network,
## dataset,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [12633] {featur,
## network,
## object} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12634] {show,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [12635] {object,
## propos,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [12636] {show,
## object,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [12637] {featur,
## object,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [12638] {featur,
## show,
## object} => {learn} 0.1000000 0.7500000 1.730769 3
## [12639] {object,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [12640] {model,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [12641] {model,
## object,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [12642] {object,
## propos,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [12643] {featur,
## object,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [12644] {model,
## object,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [12645] {featur,
## object,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [12646] {featur,
## model,
## object} => {learn} 0.1000000 0.7500000 1.730769 3
## [12647] {represent,
## show,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12648] {represent,
## object,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [12649] {represent,
## object,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [12650] {featur,
## represent,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12651] {model,
## show,
## object} => {propos} 0.1000000 0.7500000 1.500000 3
## [12652] {model,
## object,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [12653] {model,
## show,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [12654] {show,
## object,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [12655] {featur,
## show,
## object} => {propos} 0.1333333 1.0000000 2.000000 4
## [12656] {featur,
## show,
## propos} => {object} 0.1333333 1.0000000 3.750000 4
## [12657] {model,
## show,
## object} => {featur} 0.1000000 0.7500000 1.406250 3
## [12658] {featur,
## show,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [12659] {featur,
## model,
## object} => {show} 0.1000000 0.7500000 1.406250 3
## [12660] {model,
## object,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [12661] {featur,
## model,
## object} => {propos} 0.1333333 1.0000000 2.000000 4
## [12662] {model,
## object,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [12663] {network,
## object,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [12664] {model,
## network,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12665] {model,
## network,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [12666] {network,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [12667] {featur,
## network,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [12668] {featur,
## model,
## object} => {network} 0.1000000 0.7500000 1.184211 3
## [12669] {model,
## network,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [12670] {featur,
## network,
## object} => {model} 0.1000000 1.0000000 1.875000 3
## [12671] {classif,
## architectur,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [12672] {classif,
## method,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [12673] {method,
## architectur,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [12674] {classif,
## method,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12675] {classif,
## architectur,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [12676] {approach,
## classif,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [12677] {approach,
## architectur,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [12678] {approach,
## classif,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12679] {classif,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [12680] {classif,
## architectur,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [12681] {architectur,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [12682] {classif,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12683] {classif,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12684] {classif,
## network,
## architectur} => {neural} 0.1000000 0.7500000 2.250000 3
## [12685] {network,
## architectur,
## neural} => {classif} 0.1000000 0.7500000 2.812500 3
## [12686] {classif,
## network,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12687] {classif,
## method,
## architectur} => {approach} 0.1000000 1.0000000 2.500000 3
## [12688] {approach,
## classif,
## architectur} => {method} 0.1000000 1.0000000 2.727273 3
## [12689] {approach,
## method,
## architectur} => {classif} 0.1000000 1.0000000 3.750000 3
## [12690] {classif,
## method,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [12691] {classif,
## architectur,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [12692] {method,
## architectur,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [12693] {classif,
## method,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12694] {classif,
## method,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [12695] {classif,
## network,
## architectur} => {method} 0.1000000 0.7500000 2.045455 3
## [12696] {method,
## network,
## architectur} => {classif} 0.1000000 0.7500000 2.812500 3
## [12697] {classif,
## method,
## network} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12698] {approach,
## classif,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [12699] {classif,
## architectur,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [12700] {approach,
## architectur,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [12701] {approach,
## classif,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12702] {approach,
## classif,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [12703] {classif,
## network,
## architectur} => {approach} 0.1000000 0.7500000 1.875000 3
## [12704] {approach,
## network,
## architectur} => {classif} 0.1000000 1.0000000 3.750000 3
## [12705] {approach,
## classif,
## network} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12706] {classif,
## architectur,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [12707] {classif,
## architectur,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12708] {architectur,
## dataset,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [12709] {classif,
## dataset,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12710] {classif,
## architectur,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [12711] {classif,
## network,
## architectur} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12712] {classif,
## network,
## dataset} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12713] {classif,
## architectur,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [12714] {classif,
## featur,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [12715] {featur,
## architectur,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [12716] {classif,
## architectur,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [12717] {classif,
## network,
## architectur} => {propos} 0.1333333 1.0000000 2.000000 4
## [12718] {network,
## architectur,
## propos} => {classif} 0.1333333 0.8000000 3.000000 4
## [12719] {classif,
## network,
## propos} => {architectur} 0.1333333 1.0000000 3.750000 4
## [12720] {classif,
## featur,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [12721] {classif,
## network,
## architectur} => {featur} 0.1000000 0.7500000 1.406250 3
## [12722] {classif,
## featur,
## network} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12723] {model,
## architectur,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [12724] {featur,
## architectur,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [12725] {featur,
## model,
## architectur} => {recognit} 0.1000000 1.0000000 3.333333 3
## [12726] {featur,
## model,
## recognit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12727] {architectur,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [12728] {algorithm,
## architectur,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [12729] {algorithm,
## architectur,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [12730] {algorithm,
## improv,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12731] {architectur,
## improv,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [12732] {architectur,
## improv,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [12733] {architectur,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [12734] {improv,
## neural,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12735] {architectur,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12736] {architectur,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [12737] {architectur,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [12738] {architectur,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12739] {network,
## architectur,
## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [12740] {network,
## architectur,
## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [12741] {network,
## improv,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12742] {algorithm,
## architectur,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [12743] {architectur,
## improv,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12744] {algorithm,
## architectur,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [12745] {algorithm,
## improv,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12746] {algorithm,
## architectur,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12747] {architectur,
## dataset,
## improv} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [12748] {algorithm,
## architectur,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [12749] {algorithm,
## dataset,
## improv} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12750] {algorithm,
## architectur,
## improv} => {network} 0.1000000 1.0000000 1.578947 3
## [12751] {network,
## architectur,
## improv} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12752] {network,
## algorithm,
## architectur} => {improv} 0.1000000 1.0000000 3.333333 3
## [12753] {network,
## algorithm,
## improv} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12754] {architectur,
## improv,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [12755] {architectur,
## improv,
## perform} => {work} 0.1000000 0.7500000 1.875000 3
## [12756] {architectur,
## perform,
## work} => {improv} 0.1000000 0.7500000 2.500000 3
## [12757] {improv,
## perform,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12758] {architectur,
## improv,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [12759] {network,
## architectur,
## improv} => {work} 0.1000000 0.7500000 1.875000 3
## [12760] {network,
## improv,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12761] {architectur,
## improv,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12762] {architectur,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [12763] {architectur,
## dataset,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [12764] {architectur,
## improv,
## perform} => {network} 0.1333333 1.0000000 1.578947 4
## [12765] {network,
## architectur,
## improv} => {perform} 0.1333333 1.0000000 2.142857 4
## [12766] {network,
## architectur,
## perform} => {improv} 0.1333333 0.8000000 2.666667 4
## [12767] {network,
## improv,
## perform} => {architectur} 0.1333333 1.0000000 3.750000 4
## [12768] {architectur,
## dataset,
## improv} => {network} 0.1000000 1.0000000 1.578947 3
## [12769] {network,
## architectur,
## improv} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12770] {network,
## dataset,
## improv} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12771] {architectur,
## neural,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [12772] {featur,
## architectur,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [12773] {featur,
## architectur,
## neural} => {result} 0.1000000 1.0000000 3.000000 3
## [12774] {featur,
## neural,
## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12775] {architectur,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [12776] {network,
## architectur,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [12777] {network,
## architectur,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [12778] {algorithm,
## architectur,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [12779] {featur,
## architectur,
## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12780] {featur,
## algorithm,
## architectur} => {result} 0.1000000 1.0000000 3.000000 3
## [12781] {featur,
## algorithm,
## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12782] {represent,
## architectur,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [12783] {featur,
## architectur,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [12784] {featur,
## represent,
## architectur} => {result} 0.1000000 1.0000000 3.000000 3
## [12785] {featur,
## architectur,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [12786] {network,
## architectur,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [12787] {featur,
## network,
## result} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12788] {method,
## architectur,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [12789] {approach,
## architectur,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [12790] {approach,
## method,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [12791] {approach,
## method,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12792] {method,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [12793] {architectur,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [12794] {method,
## architectur,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [12795] {method,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12796] {method,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12797] {network,
## architectur,
## neural} => {method} 0.1000000 0.7500000 2.045455 3
## [12798] {method,
## network,
## architectur} => {neural} 0.1000000 0.7500000 2.250000 3
## [12799] {method,
## network,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12800] {algorithm,
## architectur,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [12801] {architectur,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [12802] {algorithm,
## architectur,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [12803] {algorithm,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12804] {algorithm,
## architectur,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12805] {architectur,
## dataset,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [12806] {algorithm,
## architectur,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [12807] {algorithm,
## dataset,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12808] {algorithm,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12809] {network,
## architectur,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12810] {network,
## algorithm,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [12811] {train,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12812] {network,
## architectur,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [12813] {network,
## train,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [12814] {approach,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [12815] {architectur,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [12816] {approach,
## architectur,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [12817] {approach,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12818] {network,
## architectur,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [12819] {approach,
## network,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [12820] {architectur,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12821] {architectur,
## dataset,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [12822] {architectur,
## dataset,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [12823] {dataset,
## neural,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12824] {architectur,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [12825] {network,
## architectur,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [12826] {network,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12827] {architectur,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12828] {network,
## architectur,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12829] {architectur,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [12830] {network,
## architectur,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [12831] {featur,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12832] {network,
## architectur,
## neural} => {featur} 0.1000000 0.7500000 1.406250 3
## [12833] {featur,
## network,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12834] {approach,
## method,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [12835] {method,
## architectur,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [12836] {approach,
## architectur,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [12837] {approach,
## method,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [12838] {method,
## network,
## architectur} => {approach} 0.1000000 0.7500000 1.875000 3
## [12839] {approach,
## network,
## architectur} => {method} 0.1000000 1.0000000 2.727273 3
## [12840] {method,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12841] {method,
## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [12842] {architectur,
## dataset,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [12843] {method,
## architectur,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [12844] {method,
## architectur,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [12845] {architectur,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [12846] {method,
## architectur,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [12847] {method,
## network,
## architectur} => {perform} 0.1000000 0.7500000 1.607143 3
## [12848] {method,
## network,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12849] {method,
## architectur,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [12850] {method,
## architectur,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12851] {architectur,
## dataset,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [12852] {method,
## architectur,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [12853] {method,
## network,
## architectur} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12854] {method,
## network,
## dataset} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12855] {method,
## architectur,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [12856] {featur,
## method,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [12857] {featur,
## architectur,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [12858] {method,
## architectur,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [12859] {method,
## network,
## architectur} => {propos} 0.1333333 1.0000000 2.000000 4
## [12860] {network,
## architectur,
## propos} => {method} 0.1333333 0.8000000 2.181818 4
## [12861] {method,
## network,
## propos} => {architectur} 0.1333333 0.8000000 3.000000 4
## [12862] {featur,
## method,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [12863] {method,
## network,
## architectur} => {featur} 0.1000000 0.7500000 1.406250 3
## [12864] {algorithm,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12865] {algorithm,
## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [12866] {architectur,
## dataset,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12867] {algorithm,
## dataset,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12868] {algorithm,
## architectur,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [12869] {network,
## algorithm,
## architectur} => {perform} 0.1000000 1.0000000 2.142857 3
## [12870] {network,
## algorithm,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12871] {algorithm,
## architectur,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [12872] {network,
## algorithm,
## architectur} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12873] {network,
## algorithm,
## dataset} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12874] {task,
## architectur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [12875] {featur,
## task,
## architectur} => {learn} 0.1000000 1.0000000 2.307692 3
## [12876] {featur,
## architectur,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [12877] {approach,
## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [12878] {approach,
## network,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [12879] {architectur,
## perform,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12880] {architectur,
## dataset,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [12881] {architectur,
## dataset,
## perform} => {work} 0.1000000 0.7500000 1.875000 3
## [12882] {dataset,
## perform,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12883] {architectur,
## perform,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [12884] {network,
## architectur,
## work} => {perform} 0.1333333 0.8000000 1.714286 4
## [12885] {network,
## architectur,
## perform} => {work} 0.1333333 0.8000000 2.000000 4
## [12886] {network,
## perform,
## work} => {architectur} 0.1333333 1.0000000 3.750000 4
## [12887] {architectur,
## dataset,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [12888] {architectur,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12889] {architectur,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [12890] {architectur,
## dataset,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [12891] {featur,
## architectur,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12892] {featur,
## architectur,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [12893] {architectur,
## dataset,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [12894] {network,
## architectur,
## work} => {dataset} 0.1333333 0.8000000 1.846154 4
## [12895] {network,
## architectur,
## dataset} => {work} 0.1333333 0.8000000 2.000000 4
## [12896] {architectur,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [12897] {featur,
## architectur,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [12898] {architectur,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [12899] {architectur,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12900] {architectur,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [12901] {architectur,
## dataset,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [12902] {featur,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12903] {featur,
## architectur,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [12904] {architectur,
## dataset,
## perform} => {network} 0.1333333 1.0000000 1.578947 4
## [12905] {network,
## architectur,
## perform} => {dataset} 0.1333333 0.8000000 1.846154 4
## [12906] {network,
## architectur,
## dataset} => {perform} 0.1333333 0.8000000 1.714286 4
## [12907] {network,
## dataset,
## perform} => {architectur} 0.1333333 1.0000000 3.750000 4
## [12908] {architectur,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [12909] {network,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12910] {featur,
## architectur,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [12911] {featur,
## network,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12912] {architectur,
## dataset,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [12913] {featur,
## architectur,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [12914] {featur,
## architectur,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12915] {architectur,
## dataset,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [12916] {network,
## architectur,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [12917] {network,
## architectur,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [12918] {featur,
## architectur,
## dataset} => {network} 0.1333333 1.0000000 1.578947 4
## [12919] {network,
## architectur,
## dataset} => {featur} 0.1333333 0.8000000 1.500000 4
## [12920] {featur,
## network,
## architectur} => {dataset} 0.1333333 0.8000000 1.846154 4
## [12921] {featur,
## architectur,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [12922] {network,
## architectur,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [12923] {featur,
## network,
## architectur} => {propos} 0.1333333 0.8000000 1.600000 4
## [12924] {featur,
## network,
## propos} => {architectur} 0.1333333 0.8000000 3.000000 4
## [12925] {classif,
## make,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [12926] {approach,
## classif,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [12927] {approach,
## make,
## method} => {classif} 0.1000000 0.7500000 2.812500 3
## [12928] {classif,
## make,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [12929] {classif,
## featur,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [12930] {featur,
## make,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [12931] {approach,
## classif,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [12932] {classif,
## featur,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [12933] {approach,
## classif,
## featur} => {make} 0.1000000 0.7500000 2.500000 3
## [12934] {approach,
## make,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [12935] {make,
## perform,
## problem} => {approach} 0.1333333 0.8000000 2.000000 4
## [12936] {approach,
## make,
## perform} => {problem} 0.1333333 1.0000000 3.333333 4
## [12937] {approach,
## perform,
## problem} => {make} 0.1333333 0.8000000 2.666667 4
## [12938] {approach,
## make,
## problem} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12939] {make,
## dataset,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [12940] {approach,
## make,
## dataset} => {problem} 0.1000000 1.0000000 3.333333 3
## [12941] {approach,
## dataset,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [12942] {approach,
## make,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [12943] {make,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [12944] {approach,
## make,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [12945] {approach,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [12946] {approach,
## make,
## problem} => {represent} 0.1000000 0.7500000 1.500000 3
## [12947] {make,
## represent,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [12948] {approach,
## represent,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [12949] {approach,
## make,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [12950] {make,
## propos,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [12951] {approach,
## make,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [12952] {approach,
## propos,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [12953] {approach,
## make,
## problem} => {model} 0.1000000 0.7500000 1.406250 3
## [12954] {make,
## model,
## problem} => {approach} 0.1000000 0.7500000 1.875000 3
## [12955] {approach,
## make,
## model} => {problem} 0.1000000 0.7500000 2.500000 3
## [12956] {approach,
## model,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [12957] {approach,
## make,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [12958] {featur,
## make,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [12959] {approach,
## featur,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [12960] {make,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [12961] {make,
## dataset,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [12962] {dataset,
## perform,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [12963] {make,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [12964] {make,
## perform,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [12965] {make,
## represent,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [12966] {make,
## represent,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [12967] {represent,
## perform,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [12968] {make,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [12969] {make,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [12970] {make,
## perform,
## problem} => {model} 0.1333333 0.8000000 1.500000 4
## [12971] {make,
## model,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [12972] {model,
## perform,
## problem} => {make} 0.1333333 0.8000000 2.666667 4
## [12973] {featur,
## make,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [12974] {featur,
## make,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [12975] {make,
## dataset,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [12976] {make,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12977] {make,
## dataset,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [12978] {dataset,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [12979] {make,
## dataset,
## problem} => {model} 0.1000000 1.0000000 1.875000 3
## [12980] {make,
## model,
## problem} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12981] {make,
## model,
## dataset} => {problem} 0.1000000 0.7500000 2.500000 3
## [12982] {model,
## dataset,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [12983] {make,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [12984] {make,
## model,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [12985] {make,
## model,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [12986] {model,
## problem,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [12987] {make,
## represent,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [12988] {make,
## propos,
## problem} => {represent} 0.1000000 1.0000000 2.000000 3
## [12989] {represent,
## propos,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [12990] {make,
## method,
## paper} => {approach} 0.1000000 0.7500000 1.875000 3
## [12991] {approach,
## make,
## paper} => {method} 0.1000000 1.0000000 2.727273 3
## [12992] {approach,
## make,
## method} => {paper} 0.1000000 0.7500000 2.250000 3
## [12993] {approach,
## method,
## paper} => {make} 0.1000000 1.0000000 3.333333 3
## [12994] {make,
## method,
## paper} => {represent} 0.1000000 0.7500000 1.500000 3
## [12995] {make,
## paper,
## represent} => {method} 0.1000000 0.7500000 2.045455 3
## [12996] {make,
## method,
## represent} => {paper} 0.1000000 1.0000000 3.000000 3
## [12997] {method,
## paper,
## represent} => {make} 0.1000000 1.0000000 3.333333 3
## [12998] {make,
## method,
## paper} => {show} 0.1000000 0.7500000 1.406250 3
## [12999] {make,
## paper,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [13000] {make,
## method,
## show} => {paper} 0.1000000 0.7500000 2.250000 3
## [13001] {method,
## paper,
## show} => {make} 0.1000000 1.0000000 3.333333 3
## [13002] {make,
## method,
## paper} => {model} 0.1000000 0.7500000 1.406250 3
## [13003] {make,
## model,
## paper} => {method} 0.1000000 0.7500000 2.045455 3
## [13004] {make,
## method,
## model} => {paper} 0.1000000 0.7500000 2.250000 3
## [13005] {method,
## model,
## paper} => {make} 0.1000000 1.0000000 3.333333 3
## [13006] {make,
## paper,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [13007] {make,
## paper,
## represent} => {train} 0.1000000 0.7500000 1.875000 3
## [13008] {make,
## represent,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [13009] {paper,
## represent,
## train} => {make} 0.1000000 0.7500000 2.500000 3
## [13010] {make,
## paper,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [13011] {featur,
## make,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [13012] {featur,
## make,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [13013] {approach,
## make,
## paper} => {represent} 0.1000000 1.0000000 2.000000 3
## [13014] {make,
## paper,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [13015] {approach,
## paper,
## represent} => {make} 0.1000000 1.0000000 3.333333 3
## [13016] {make,
## paper,
## perform} => {model} 0.1000000 1.0000000 1.875000 3
## [13017] {make,
## model,
## paper} => {perform} 0.1000000 0.7500000 1.607143 3
## [13018] {model,
## paper,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [13019] {data,
## make,
## paper} => {model} 0.1000000 1.0000000 1.875000 3
## [13020] {make,
## model,
## paper} => {data} 0.1000000 0.7500000 1.730769 3
## [13021] {data,
## make,
## model} => {paper} 0.1000000 0.7500000 2.250000 3
## [13022] {data,
## model,
## paper} => {make} 0.1000000 0.7500000 2.500000 3
## [13023] {make,
## paper,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [13024] {make,
## paper,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [13025] {make,
## represent,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [13026] {paper,
## represent,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [13027] {make,
## paper,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [13028] {make,
## paper,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [13029] {make,
## propos,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [13030] {paper,
## propos,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [13031] {make,
## paper,
## represent} => {propos} 0.1000000 0.7500000 1.500000 3
## [13032] {make,
## paper,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [13033] {paper,
## represent,
## propos} => {make} 0.1000000 1.0000000 3.333333 3
## [13034] {make,
## paper,
## represent} => {model} 0.1000000 0.7500000 1.406250 3
## [13035] {make,
## model,
## paper} => {represent} 0.1000000 0.7500000 1.500000 3
## [13036] {make,
## model,
## represent} => {paper} 0.1000000 0.7500000 2.250000 3
## [13037] {model,
## paper,
## represent} => {make} 0.1000000 0.7500000 2.500000 3
## [13038] {make,
## paper,
## represent} => {featur} 0.1000000 0.7500000 1.406250 3
## [13039] {featur,
## make,
## paper} => {represent} 0.1000000 1.0000000 2.000000 3
## [13040] {featur,
## paper,
## represent} => {make} 0.1000000 0.7500000 2.500000 3
## [13041] {make,
## paper,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [13042] {make,
## model,
## paper} => {show} 0.1000000 0.7500000 1.406250 3
## [13043] {make,
## model,
## show} => {paper} 0.1000000 0.7500000 2.250000 3
## [13044] {make,
## improv,
## perform} => {model} 0.1000000 1.0000000 1.875000 3
## [13045] {make,
## model,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [13046] {model,
## improv,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [13047] {approach,
## make,
## method} => {learn} 0.1000000 0.7500000 1.730769 3
## [13048] {make,
## method,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [13049] {approach,
## make,
## learn} => {method} 0.1000000 0.7500000 2.045455 3
## [13050] {approach,
## method,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [13051] {approach,
## make,
## method} => {represent} 0.1000000 0.7500000 1.500000 3
## [13052] {make,
## method,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [13053] {approach,
## method,
## represent} => {make} 0.1000000 0.7500000 2.500000 3
## [13054] {approach,
## make,
## method} => {show} 0.1000000 0.7500000 1.406250 3
## [13055] {make,
## method,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [13056] {approach,
## make,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [13057] {approach,
## make,
## method} => {model} 0.1000000 0.7500000 1.406250 3
## [13058] {make,
## method,
## model} => {approach} 0.1000000 0.7500000 1.875000 3
## [13059] {approach,
## make,
## model} => {method} 0.1000000 0.7500000 2.045455 3
## [13060] {approach,
## make,
## method} => {featur} 0.1000000 0.7500000 1.406250 3
## [13061] {featur,
## make,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [13062] {make,
## method,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [13063] {make,
## method,
## show} => {perform} 0.1000000 0.7500000 1.607143 3
## [13064] {make,
## show,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [13065] {make,
## method,
## perform} => {model} 0.1000000 1.0000000 1.875000 3
## [13066] {make,
## method,
## model} => {perform} 0.1000000 0.7500000 1.607143 3
## [13067] {method,
## model,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [13068] {make,
## method,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [13069] {make,
## method,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [13070] {make,
## model,
## show} => {method} 0.1333333 1.0000000 2.727273 4
## [13071] {approach,
## make,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [13072] {data,
## make,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [13073] {approach,
## data,
## make} => {task} 0.1000000 1.0000000 2.727273 3
## [13074] {approach,
## make,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [13075] {make,
## represent,
## task} => {approach} 0.1333333 1.0000000 2.500000 4
## [13076] {approach,
## make,
## represent} => {task} 0.1333333 0.8000000 2.181818 4
## [13077] {approach,
## make,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [13078] {make,
## task,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [13079] {approach,
## make,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [13080] {approach,
## make,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [13081] {featur,
## make,
## task} => {approach} 0.1333333 1.0000000 2.500000 4
## [13082] {approach,
## featur,
## make} => {task} 0.1333333 0.8000000 2.181818 4
## [13083] {approach,
## featur,
## task} => {make} 0.1333333 0.8000000 2.666667 4
## [13084] {data,
## make,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [13085] {make,
## represent,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [13086] {data,
## make,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [13087] {data,
## make,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [13088] {featur,
## make,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [13089] {data,
## featur,
## make} => {task} 0.1000000 0.7500000 2.045455 3
## [13090] {make,
## represent,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [13091] {make,
## task,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [13092] {make,
## represent,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [13093] {featur,
## make,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [13094] {featur,
## make,
## represent} => {task} 0.1333333 0.8000000 2.181818 4
## [13095] {make,
## task,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [13096] {featur,
## make,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [13097] {featur,
## make,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [13098] {make,
## represent,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [13099] {featur,
## make,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [13100] {approach,
## make,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13101] {approach,
## make,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [13102] {make,
## dataset,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [13103] {approach,
## make,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [13104] {approach,
## make,
## learn} => {perform} 0.1000000 0.7500000 1.607143 3
## [13105] {make,
## perform,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [13106] {approach,
## perform,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [13107] {approach,
## make,
## perform} => {represent} 0.1000000 0.7500000 1.500000 3
## [13108] {make,
## represent,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [13109] {approach,
## represent,
## perform} => {make} 0.1000000 1.0000000 3.333333 3
## [13110] {approach,
## make,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [13111] {approach,
## make,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [13112] {make,
## perform,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [13113] {approach,
## make,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [13114] {approach,
## make,
## model} => {perform} 0.1000000 0.7500000 1.607143 3
## [13115] {approach,
## model,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [13116] {approach,
## make,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [13117] {featur,
## make,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [13118] {approach,
## featur,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [13119] {approach,
## data,
## make} => {represent} 0.1000000 1.0000000 2.000000 3
## [13120] {data,
## make,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [13121] {approach,
## data,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [13122] {data,
## featur,
## make} => {approach} 0.1000000 0.7500000 1.875000 3
## [13123] {approach,
## data,
## featur} => {make} 0.1000000 0.7500000 2.500000 3
## [13124] {approach,
## make,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [13125] {approach,
## make,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13126] {make,
## dataset,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [13127] {approach,
## make,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [13128] {approach,
## make,
## model} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13129] {make,
## model,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [13130] {approach,
## make,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13131] {make,
## represent,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [13132] {approach,
## make,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13133] {approach,
## make,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [13134] {make,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [13135] {approach,
## make,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [13136] {approach,
## make,
## model} => {learn} 0.1000000 0.7500000 1.730769 3
## [13137] {make,
## model,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [13138] {approach,
## make,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13139] {featur,
## make,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [13140] {approach,
## make,
## represent} => {propos} 0.1333333 0.8000000 1.600000 4
## [13141] {approach,
## make,
## propos} => {represent} 0.1333333 1.0000000 2.000000 4
## [13142] {make,
## represent,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [13143] {approach,
## make,
## model} => {represent} 0.1000000 0.7500000 1.500000 3
## [13144] {make,
## model,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [13145] {approach,
## make,
## represent} => {featur} 0.1333333 0.8000000 1.500000 4
## [13146] {approach,
## featur,
## make} => {represent} 0.1333333 0.8000000 1.600000 4
## [13147] {featur,
## make,
## represent} => {approach} 0.1333333 0.8000000 2.000000 4
## [13148] {approach,
## make,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [13149] {approach,
## make,
## model} => {show} 0.1000000 0.7500000 1.406250 3
## [13150] {make,
## model,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [13151] {approach,
## make,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [13152] {featur,
## make,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [13153] {approach,
## make,
## model} => {featur} 0.1000000 0.7500000 1.406250 3
## [13154] {featur,
## make,
## model} => {approach} 0.1000000 0.7500000 1.875000 3
## [13155] {data,
## make,
## perform} => {represent} 0.1000000 0.7500000 1.500000 3
## [13156] {make,
## represent,
## perform} => {data} 0.1000000 0.7500000 1.730769 3
## [13157] {data,
## make,
## represent} => {perform} 0.1000000 0.7500000 1.607143 3
## [13158] {data,
## represent,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [13159] {data,
## make,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [13160] {make,
## perform,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [13161] {data,
## make,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [13162] {data,
## perform,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [13163] {data,
## make,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [13164] {data,
## make,
## model} => {perform} 0.1000000 0.7500000 1.607143 3
## [13165] {data,
## model,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [13166] {data,
## make,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [13167] {featur,
## make,
## perform} => {data} 0.1000000 0.7500000 1.730769 3
## [13168] {data,
## featur,
## make} => {perform} 0.1000000 0.7500000 1.607143 3
## [13169] {make,
## dataset,
## perform} => {learn} 0.1333333 1.0000000 2.307692 4
## [13170] {make,
## perform,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [13171] {make,
## dataset,
## learn} => {perform} 0.1333333 1.0000000 2.142857 4
## [13172] {dataset,
## perform,
## learn} => {make} 0.1333333 1.0000000 3.333333 4
## [13173] {make,
## dataset,
## perform} => {represent} 0.1000000 0.7500000 1.500000 3
## [13174] {make,
## represent,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13175] {make,
## represent,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [13176] {represent,
## dataset,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [13177] {make,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [13178] {make,
## perform,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13179] {make,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [13180] {make,
## dataset,
## perform} => {model} 0.1333333 1.0000000 1.875000 4
## [13181] {make,
## model,
## dataset} => {perform} 0.1333333 1.0000000 2.142857 4
## [13182] {model,
## dataset,
## perform} => {make} 0.1333333 0.8000000 2.666667 4
## [13183] {make,
## dataset,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [13184] {featur,
## make,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13185] {featur,
## make,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [13186] {make,
## perform,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13187] {make,
## represent,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [13188] {make,
## represent,
## learn} => {perform} 0.1000000 0.7500000 1.607143 3
## [13189] {represent,
## perform,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [13190] {make,
## perform,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13191] {make,
## perform,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [13192] {make,
## propos,
## learn} => {perform} 0.1000000 0.7500000 1.607143 3
## [13193] {make,
## perform,
## learn} => {model} 0.1333333 1.0000000 1.875000 4
## [13194] {make,
## model,
## learn} => {perform} 0.1333333 1.0000000 2.142857 4
## [13195] {model,
## perform,
## learn} => {make} 0.1333333 0.8000000 2.666667 4
## [13196] {make,
## perform,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13197] {featur,
## make,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [13198] {featur,
## make,
## learn} => {perform} 0.1000000 0.7500000 1.607143 3
## [13199] {make,
## represent,
## perform} => {propos} 0.1333333 1.0000000 2.000000 4
## [13200] {make,
## perform,
## propos} => {represent} 0.1333333 1.0000000 2.000000 4
## [13201] {make,
## represent,
## propos} => {perform} 0.1333333 0.8000000 1.714286 4
## [13202] {make,
## represent,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [13203] {make,
## model,
## represent} => {perform} 0.1000000 0.7500000 1.607143 3
## [13204] {model,
## represent,
## perform} => {make} 0.1000000 1.0000000 3.333333 3
## [13205] {make,
## represent,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [13206] {featur,
## make,
## perform} => {represent} 0.1000000 0.7500000 1.500000 3
## [13207] {make,
## show,
## perform} => {model} 0.1000000 1.0000000 1.875000 3
## [13208] {make,
## model,
## show} => {perform} 0.1000000 0.7500000 1.607143 3
## [13209] {make,
## perform,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [13210] {make,
## model,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [13211] {make,
## perform,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [13212] {featur,
## make,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [13213] {featur,
## make,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [13214] {featur,
## make,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [13215] {featur,
## make,
## model} => {perform} 0.1000000 0.7500000 1.607143 3
## [13216] {data,
## make,
## represent} => {propos} 0.1000000 0.7500000 1.500000 3
## [13217] {data,
## make,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [13218] {data,
## make,
## represent} => {model} 0.1000000 0.7500000 1.406250 3
## [13219] {data,
## make,
## model} => {represent} 0.1000000 0.7500000 1.500000 3
## [13220] {make,
## model,
## represent} => {data} 0.1000000 0.7500000 1.730769 3
## [13221] {data,
## make,
## represent} => {featur} 0.1333333 1.0000000 1.875000 4
## [13222] {data,
## featur,
## make} => {represent} 0.1333333 1.0000000 2.000000 4
## [13223] {featur,
## make,
## represent} => {data} 0.1333333 0.8000000 1.846154 4
## [13224] {data,
## make,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [13225] {data,
## featur,
## make} => {propos} 0.1000000 0.7500000 1.500000 3
## [13226] {featur,
## make,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [13227] {data,
## make,
## model} => {featur} 0.1000000 0.7500000 1.406250 3
## [13228] {data,
## featur,
## make} => {model} 0.1000000 0.7500000 1.406250 3
## [13229] {featur,
## make,
## model} => {data} 0.1000000 0.7500000 1.730769 3
## [13230] {make,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13231] {make,
## represent,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [13232] {make,
## represent,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13233] {make,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13234] {make,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [13235] {make,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13236] {make,
## dataset,
## learn} => {model} 0.1333333 1.0000000 1.875000 4
## [13237] {make,
## model,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [13238] {make,
## model,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [13239] {make,
## dataset,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13240] {featur,
## make,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [13241] {featur,
## make,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13242] {make,
## represent,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [13243] {make,
## dataset,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [13244] {make,
## represent,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [13245] {make,
## model,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [13246] {make,
## model,
## represent} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13247] {model,
## represent,
## dataset} => {make} 0.1000000 0.7500000 2.500000 3
## [13248] {make,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [13249] {make,
## model,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [13250] {make,
## model,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13251] {make,
## model,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [13252] {featur,
## make,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [13253] {featur,
## make,
## model} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13254] {make,
## represent,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [13255] {make,
## propos,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [13256] {make,
## represent,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [13257] {make,
## represent,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [13258] {make,
## model,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13259] {make,
## model,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [13260] {make,
## represent,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13261] {featur,
## make,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13262] {make,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [13263] {make,
## model,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13264] {make,
## model,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [13265] {make,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13266] {featur,
## make,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13267] {featur,
## make,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [13268] {make,
## model,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13269] {featur,
## make,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [13270] {featur,
## make,
## model} => {learn} 0.1000000 0.7500000 1.730769 3
## [13271] {make,
## model,
## represent} => {propos} 0.1000000 0.7500000 1.500000 3
## [13272] {make,
## model,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [13273] {model,
## represent,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [13274] {make,
## represent,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [13275] {featur,
## make,
## represent} => {propos} 0.1333333 0.8000000 1.600000 4
## [13276] {featur,
## make,
## propos} => {represent} 0.1333333 1.0000000 2.000000 4
## [13277] {make,
## model,
## represent} => {featur} 0.1000000 0.7500000 1.406250 3
## [13278] {featur,
## make,
## model} => {represent} 0.1000000 0.7500000 1.500000 3
## [13279] {classif,
## method,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13280] {classif,
## perform,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [13281] {classif,
## method,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [13282] {method,
## perform,
## problem} => {classif} 0.1000000 0.7500000 2.812500 3
## [13283] {classif,
## method,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [13284] {classif,
## show,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [13285] {classif,
## perform,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [13286] {classif,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [13287] {classif,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [13288] {classif,
## perform,
## problem} => {show} 0.1333333 1.0000000 1.875000 4
## [13289] {classif,
## show,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [13290] {classif,
## show,
## perform} => {problem} 0.1333333 0.8000000 2.666667 4
## [13291] {show,
## perform,
## problem} => {classif} 0.1333333 0.8000000 3.000000 4
## [13292] {classif,
## perform,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [13293] {classif,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13294] {classif,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [13295] {classif,
## perform,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [13296] {classif,
## featur,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13297] {classif,
## featur,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [13298] {classif,
## problem,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [13299] {classif,
## show,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [13300] {classif,
## show,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [13301] {show,
## problem,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [13302] {classif,
## problem,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [13303] {classif,
## featur,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [13304] {featur,
## problem,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [13305] {classif,
## show,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [13306] {classif,
## propos,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [13307] {classif,
## show,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [13308] {show,
## propos,
## problem} => {classif} 0.1000000 0.7500000 2.812500 3
## [13309] {classif,
## show,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [13310] {classif,
## featur,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [13311] {featur,
## show,
## problem} => {classif} 0.1000000 1.0000000 3.750000 3
## [13312] {classif,
## paper,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [13313] {classif,
## paper,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [13314] {classif,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [13315] {paper,
## task,
## train} => {classif} 0.1000000 0.7500000 2.812500 3
## [13316] {classif,
## paper,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [13317] {classif,
## featur,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [13318] {classif,
## featur,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [13319] {featur,
## paper,
## task} => {classif} 0.1000000 0.7500000 2.812500 3
## [13320] {classif,
## paper,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [13321] {classif,
## featur,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [13322] {classif,
## featur,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [13323] {classif,
## propos,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [13324] {classif,
## featur,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [13325] {featur,
## propos,
## recognit} => {classif} 0.1000000 0.7500000 2.812500 3
## [13326] {classif,
## method,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [13327] {approach,
## classif,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [13328] {approach,
## method,
## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [13329] {classif,
## method,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [13330] {classif,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [13331] {classif,
## method,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [13332] {method,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [13333] {classif,
## method,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13334] {classif,
## dataset,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [13335] {classif,
## method,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [13336] {method,
## dataset,
## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [13337] {classif,
## method,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [13338] {classif,
## show,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [13339] {method,
## show,
## improv} => {classif} 0.1000000 0.7500000 2.812500 3
## [13340] {approach,
## classif,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [13341] {classif,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [13342] {approach,
## classif,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [13343] {approach,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [13344] {approach,
## classif,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13345] {classif,
## dataset,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [13346] {approach,
## classif,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [13347] {approach,
## dataset,
## improv} => {classif} 0.1000000 0.7500000 2.812500 3
## [13348] {approach,
## classif,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [13349] {classif,
## show,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [13350] {approach,
## classif,
## show} => {improv} 0.1000000 0.7500000 2.500000 3
## [13351] {approach,
## show,
## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [13352] {classif,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13353] {classif,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [13354] {classif,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [13355] {classif,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [13356] {classif,
## show,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [13357] {show,
## improv,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [13358] {classif,
## dataset,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [13359] {classif,
## show,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13360] {classif,
## show,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [13361] {show,
## dataset,
## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [13362] {classif,
## method,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [13363] {approach,
## classif,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [13364] {approach,
## method,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [13365] {classif,
## method,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [13366] {classif,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [13367] {classif,
## method,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [13368] {method,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [13369] {classif,
## method,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [13370] {classif,
## network,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [13371] {classif,
## method,
## network} => {neural} 0.1000000 0.7500000 2.250000 3
## [13372] {method,
## network,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [13373] {approach,
## classif,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [13374] {classif,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [13375] {approach,
## classif,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [13376] {approach,
## classif,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [13377] {classif,
## network,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [13378] {approach,
## classif,
## network} => {neural} 0.1000000 0.7500000 2.250000 3
## [13379] {classif,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [13380] {classif,
## network,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [13381] {classif,
## network,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [13382] {classif,
## method,
## train} => {approach} 0.1000000 1.0000000 2.500000 3
## [13383] {approach,
## classif,
## train} => {method} 0.1000000 1.0000000 2.727273 3
## [13384] {approach,
## method,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [13385] {classif,
## method,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [13386] {classif,
## method,
## network} => {train} 0.1000000 0.7500000 1.875000 3
## [13387] {classif,
## network,
## train} => {method} 0.1000000 1.0000000 2.727273 3
## [13388] {method,
## network,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [13389] {classif,
## method,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [13390] {approach,
## classif,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [13391] {approach,
## method,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [13392] {classif,
## method,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [13393] {approach,
## classif,
## dataset} => {method} 0.1000000 1.0000000 2.727273 3
## [13394] {approach,
## classif,
## method} => {show} 0.1333333 0.8000000 1.500000 4
## [13395] {classif,
## method,
## show} => {approach} 0.1333333 0.8000000 2.000000 4
## [13396] {approach,
## classif,
## show} => {method} 0.1333333 1.0000000 2.727273 4
## [13397] {classif,
## method,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [13398] {approach,
## classif,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [13399] {classif,
## method,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [13400] {approach,
## classif,
## model} => {method} 0.1000000 1.0000000 2.727273 3
## [13401] {approach,
## classif,
## method} => {featur} 0.1333333 0.8000000 1.500000 4
## [13402] {classif,
## featur,
## method} => {approach} 0.1333333 0.8000000 2.000000 4
## [13403] {approach,
## classif,
## featur} => {method} 0.1333333 1.0000000 2.727273 4
## [13404] {approach,
## featur,
## method} => {classif} 0.1333333 0.8000000 3.000000 4
## [13405] {approach,
## classif,
## method} => {network} 0.1333333 0.8000000 1.263158 4
## [13406] {classif,
## method,
## network} => {approach} 0.1333333 1.0000000 2.500000 4
## [13407] {approach,
## classif,
## network} => {method} 0.1333333 1.0000000 2.727273 4
## [13408] {approach,
## method,
## network} => {classif} 0.1333333 0.8000000 3.000000 4
## [13409] {classif,
## method,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13410] {classif,
## method,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [13411] {classif,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [13412] {classif,
## method,
## perform} => {show} 0.1333333 1.0000000 1.875000 4
## [13413] {classif,
## method,
## show} => {perform} 0.1333333 0.8000000 1.714286 4
## [13414] {classif,
## show,
## perform} => {method} 0.1333333 0.8000000 2.181818 4
## [13415] {classif,
## method,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [13416] {classif,
## method,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [13417] {classif,
## perform,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [13418] {classif,
## method,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [13419] {classif,
## featur,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [13420] {featur,
## method,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [13421] {classif,
## method,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [13422] {classif,
## show,
## dataset} => {method} 0.1000000 1.0000000 2.727273 3
## [13423] {classif,
## method,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [13424] {featur,
## method,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [13425] {classif,
## method,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [13426] {classif,
## featur,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [13427] {featur,
## method,
## represent} => {classif} 0.1000000 0.7500000 2.812500 3
## [13428] {classif,
## method,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [13429] {classif,
## show,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [13430] {classif,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [13431] {classif,
## model,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [13432] {classif,
## method,
## show} => {featur} 0.1333333 0.8000000 1.500000 4
## [13433] {classif,
## featur,
## method} => {show} 0.1333333 0.8000000 1.500000 4
## [13434] {classif,
## featur,
## show} => {method} 0.1333333 0.8000000 2.181818 4
## [13435] {featur,
## method,
## show} => {classif} 0.1333333 0.8000000 3.000000 4
## [13436] {classif,
## method,
## network} => {show} 0.1000000 0.7500000 1.406250 3
## [13437] {classif,
## network,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [13438] {method,
## network,
## show} => {classif} 0.1000000 0.7500000 2.812500 3
## [13439] {classif,
## method,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [13440] {classif,
## method,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [13441] {classif,
## method,
## network} => {propos} 0.1000000 0.7500000 1.500000 3
## [13442] {classif,
## network,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [13443] {classif,
## method,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [13444] {featur,
## method,
## model} => {classif} 0.1000000 0.7500000 2.812500 3
## [13445] {classif,
## method,
## network} => {featur} 0.1000000 0.7500000 1.406250 3
## [13446] {classif,
## featur,
## network} => {method} 0.1000000 0.7500000 2.045455 3
## [13447] {classif,
## algorithm,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [13448] {classif,
## show,
## algorithm} => {perform} 0.1000000 1.0000000 2.142857 3
## [13449] {show,
## algorithm,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [13450] {classif,
## algorithm,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [13451] {classif,
## algorithm,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [13452] {classif,
## perform,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [13453] {algorithm,
## perform,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [13454] {classif,
## show,
## algorithm} => {propos} 0.1000000 1.0000000 2.000000 3
## [13455] {classif,
## algorithm,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [13456] {classif,
## show,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [13457] {show,
## algorithm,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [13458] {classif,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [13459] {classif,
## featur,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [13460] {classif,
## featur,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [13461] {featur,
## task,
## train} => {classif} 0.1000000 0.7500000 2.812500 3
## [13462] {classif,
## data,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [13463] {classif,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [13464] {classif,
## data,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [13465] {classif,
## data,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [13466] {classif,
## featur,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [13467] {classif,
## data,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [13468] {classif,
## task,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [13469] {classif,
## task,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [13470] {classif,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [13471] {classif,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [13472] {classif,
## featur,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [13473] {classif,
## task,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [13474] {classif,
## featur,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [13475] {classif,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [13476] {classif,
## featur,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [13477] {classif,
## featur,
## task} => {network} 0.1000000 0.7500000 1.184211 3
## [13478] {classif,
## network,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [13479] {classif,
## featur,
## network} => {task} 0.1000000 0.7500000 2.045455 3
## [13480] {approach,
## classif,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [13481] {classif,
## network,
## train} => {approach} 0.1000000 1.0000000 2.500000 3
## [13482] {approach,
## classif,
## network} => {train} 0.1000000 0.7500000 1.875000 3
## [13483] {approach,
## classif,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13484] {approach,
## classif,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [13485] {classif,
## dataset,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [13486] {approach,
## classif,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [13487] {approach,
## classif,
## show} => {perform} 0.1000000 0.7500000 1.607143 3
## [13488] {approach,
## show,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [13489] {approach,
## classif,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [13490] {approach,
## classif,
## show} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13491] {classif,
## show,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [13492] {approach,
## classif,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [13493] {approach,
## classif,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [13494] {classif,
## model,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [13495] {approach,
## classif,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [13496] {approach,
## classif,
## featur} => {show} 0.1000000 0.7500000 1.406250 3
## [13497] {approach,
## classif,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [13498] {approach,
## classif,
## network} => {show} 0.1000000 0.7500000 1.406250 3
## [13499] {classif,
## network,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [13500] {approach,
## classif,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [13501] {approach,
## classif,
## network} => {propos} 0.1000000 0.7500000 1.500000 3
## [13502] {classif,
## network,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [13503] {approach,
## classif,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [13504] {approach,
## classif,
## featur} => {model} 0.1000000 0.7500000 1.406250 3
## [13505] {approach,
## classif,
## featur} => {network} 0.1000000 0.7500000 1.184211 3
## [13506] {approach,
## classif,
## network} => {featur} 0.1000000 0.7500000 1.406250 3
## [13507] {classif,
## featur,
## network} => {approach} 0.1000000 0.7500000 1.875000 3
## [13508] {classif,
## dataset,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [13509] {classif,
## show,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [13510] {show,
## dataset,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [13511] {classif,
## perform,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [13512] {classif,
## show,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [13513] {show,
## perform,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [13514] {classif,
## perform,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [13515] {classif,
## featur,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [13516] {classif,
## show,
## perform} => {propos} 0.1333333 0.8000000 1.600000 4
## [13517] {classif,
## perform,
## propos} => {show} 0.1333333 1.0000000 1.875000 4
## [13518] {classif,
## show,
## propos} => {perform} 0.1333333 1.0000000 2.142857 4
## [13519] {classif,
## model,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [13520] {classif,
## model,
## show} => {perform} 0.1000000 0.7500000 1.607143 3
## [13521] {classif,
## show,
## perform} => {featur} 0.1333333 0.8000000 1.500000 4
## [13522] {classif,
## featur,
## perform} => {show} 0.1333333 1.0000000 1.875000 4
## [13523] {classif,
## featur,
## show} => {perform} 0.1333333 0.8000000 1.714286 4
## [13524] {featur,
## show,
## perform} => {classif} 0.1333333 1.0000000 3.750000 4
## [13525] {classif,
## perform,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [13526] {classif,
## featur,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [13527] {classif,
## model,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [13528] {classif,
## featur,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [13529] {classif,
## data,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [13530] {classif,
## data,
## featur} => {model} 0.1000000 1.0000000 1.875000 3
## [13531] {classif,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [13532] {classif,
## network,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [13533] {classif,
## network,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13534] {classif,
## model,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [13535] {classif,
## featur,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [13536] {classif,
## show,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [13537] {classif,
## propos,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [13538] {classif,
## featur,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [13539] {classif,
## featur,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [13540] {classif,
## model,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [13541] {classif,
## show,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [13542] {featur,
## show,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [13543] {classif,
## model,
## show} => {featur} 0.1333333 1.0000000 1.875000 4
## [13544] {classif,
## featur,
## show} => {model} 0.1333333 0.8000000 1.500000 4
## [13545] {classif,
## featur,
## model} => {show} 0.1333333 0.8000000 1.500000 4
## [13546] {classif,
## model,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [13547] {classif,
## network,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [13548] {classif,
## featur,
## network} => {propos} 0.1000000 0.7500000 1.500000 3
## [13549] {classif,
## model,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [13550] {classif,
## featur,
## network} => {model} 0.1000000 0.7500000 1.406250 3
## [13551] {method,
## improv,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [13552] {show,
## improv,
## problem} => {method} 0.1000000 1.0000000 2.727273 3
## [13553] {method,
## show,
## improv} => {problem} 0.1000000 0.7500000 2.500000 3
## [13554] {approach,
## method,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13555] {method,
## perform,
## problem} => {approach} 0.1000000 0.7500000 1.875000 3
## [13556] {approach,
## method,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [13557] {approach,
## method,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13558] {method,
## dataset,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [13559] {approach,
## dataset,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [13560] {approach,
## method,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [13561] {approach,
## show,
## problem} => {method} 0.1000000 1.0000000 2.727273 3
## [13562] {method,
## perform,
## problem} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13563] {method,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13564] {dataset,
## perform,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [13565] {method,
## perform,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [13566] {method,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [13567] {method,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [13568] {method,
## perform,
## problem} => {show} 0.1333333 1.0000000 1.875000 4
## [13569] {method,
## show,
## problem} => {perform} 0.1333333 0.8000000 1.714286 4
## [13570] {show,
## perform,
## problem} => {method} 0.1333333 0.8000000 2.181818 4
## [13571] {method,
## perform,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [13572] {method,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13573] {method,
## dataset,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [13574] {show,
## dataset,
## problem} => {method} 0.1000000 1.0000000 2.727273 3
## [13575] {method,
## problem,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [13576] {show,
## problem,
## learn} => {method} 0.1000000 0.7500000 2.045455 3
## [13577] {method,
## show,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [13578] {method,
## propos,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [13579] {show,
## propos,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [13580] {method,
## model,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [13581] {model,
## show,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [13582] {train,
## algorithm,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [13583] {show,
## algorithm,
## problem} => {train} 0.1000000 1.0000000 2.500000 3
## [13584] {show,
## train,
## problem} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [13585] {show,
## train,
## algorithm} => {problem} 0.1000000 1.0000000 3.333333 3
## [13586] {algorithm,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [13587] {algorithm,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13588] {algorithm,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [13589] {task,
## perform,
## problem} => {data} 0.1000000 1.0000000 2.307692 3
## [13590] {data,
## task,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13591] {data,
## perform,
## problem} => {task} 0.1000000 1.0000000 2.727273 3
## [13592] {data,
## task,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [13593] {task,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [13594] {task,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13595] {task,
## perform,
## propos} => {problem} 0.1000000 1.0000000 3.333333 3
## [13596] {task,
## perform,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [13597] {featur,
## task,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13598] {featur,
## task,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [13599] {data,
## task,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [13600] {task,
## propos,
## problem} => {data} 0.1000000 1.0000000 2.307692 3
## [13601] {data,
## propos,
## problem} => {task} 0.1000000 1.0000000 2.727273 3
## [13602] {data,
## task,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [13603] {featur,
## task,
## problem} => {data} 0.1000000 1.0000000 2.307692 3
## [13604] {data,
## featur,
## problem} => {task} 0.1000000 1.0000000 2.727273 3
## [13605] {task,
## propos,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [13606] {featur,
## task,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [13607] {featur,
## propos,
## problem} => {task} 0.1000000 0.7500000 2.045455 3
## [13608] {approach,
## perform,
## problem} => {dataset} 0.1333333 0.8000000 1.846154 4
## [13609] {approach,
## dataset,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [13610] {dataset,
## perform,
## problem} => {approach} 0.1333333 1.0000000 2.500000 4
## [13611] {approach,
## dataset,
## perform} => {problem} 0.1333333 0.8000000 2.666667 4
## [13612] {approach,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [13613] {approach,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [13614] {approach,
## represent,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13615] {represent,
## perform,
## problem} => {approach} 0.1000000 0.7500000 1.875000 3
## [13616] {approach,
## represent,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [13617] {approach,
## show,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13618] {approach,
## show,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [13619] {approach,
## perform,
## problem} => {propos} 0.1333333 0.8000000 1.600000 4
## [13620] {approach,
## propos,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [13621] {approach,
## perform,
## propos} => {problem} 0.1333333 0.8000000 2.666667 4
## [13622] {approach,
## model,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13623] {approach,
## model,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [13624] {approach,
## featur,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13625] {approach,
## featur,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [13626] {approach,
## dataset,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [13627] {approach,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13628] {dataset,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [13629] {approach,
## dataset,
## problem} => {show} 0.1000000 0.7500000 1.406250 3
## [13630] {approach,
## show,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13631] {show,
## dataset,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [13632] {approach,
## dataset,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [13633] {approach,
## propos,
## problem} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13634] {dataset,
## propos,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [13635] {approach,
## dataset,
## problem} => {model} 0.1000000 0.7500000 1.406250 3
## [13636] {approach,
## model,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13637] {model,
## dataset,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [13638] {approach,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [13639] {approach,
## model,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [13640] {model,
## problem,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [13641] {approach,
## represent,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [13642] {approach,
## propos,
## problem} => {represent} 0.1000000 0.7500000 1.500000 3
## [13643] {represent,
## propos,
## problem} => {approach} 0.1000000 0.7500000 1.875000 3
## [13644] {data,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [13645] {data,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13646] {data,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [13647] {data,
## perform,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [13648] {data,
## featur,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13649] {dataset,
## perform,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [13650] {dataset,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [13651] {dataset,
## perform,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [13652] {dataset,
## perform,
## problem} => {show} 0.1000000 0.7500000 1.406250 3
## [13653] {show,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13654] {show,
## dataset,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [13655] {dataset,
## perform,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [13656] {dataset,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13657] {dataset,
## perform,
## problem} => {model} 0.1000000 0.7500000 1.406250 3
## [13658] {model,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13659] {represent,
## perform,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [13660] {represent,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [13661] {represent,
## perform,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [13662] {perform,
## problem,
## learn} => {show} 0.1333333 0.8000000 1.500000 4
## [13663] {show,
## perform,
## problem} => {learn} 0.1333333 0.8000000 1.846154 4
## [13664] {show,
## problem,
## learn} => {perform} 0.1333333 1.0000000 2.142857 4
## [13665] {show,
## perform,
## learn} => {problem} 0.1333333 1.0000000 3.333333 4
## [13666] {perform,
## problem,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [13667] {propos,
## problem,
## learn} => {perform} 0.1333333 1.0000000 2.142857 4
## [13668] {perform,
## propos,
## learn} => {problem} 0.1333333 0.8000000 2.666667 4
## [13669] {perform,
## problem,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [13670] {model,
## perform,
## problem} => {learn} 0.1333333 0.8000000 1.846154 4
## [13671] {model,
## problem,
## learn} => {perform} 0.1333333 1.0000000 2.142857 4
## [13672] {model,
## perform,
## learn} => {problem} 0.1333333 0.8000000 2.666667 4
## [13673] {perform,
## problem,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [13674] {featur,
## perform,
## problem} => {learn} 0.1333333 0.8000000 1.846154 4
## [13675] {featur,
## problem,
## learn} => {perform} 0.1333333 1.0000000 2.142857 4
## [13676] {featur,
## perform,
## learn} => {problem} 0.1333333 0.8000000 2.666667 4
## [13677] {represent,
## perform,
## problem} => {propos} 0.1333333 1.0000000 2.000000 4
## [13678] {represent,
## propos,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [13679] {represent,
## perform,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [13680] {featur,
## represent,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13681] {show,
## perform,
## problem} => {propos} 0.1333333 0.8000000 1.600000 4
## [13682] {show,
## propos,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [13683] {model,
## show,
## problem} => {perform} 0.1000000 0.7500000 1.607143 3
## [13684] {featur,
## show,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13685] {featur,
## show,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [13686] {model,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13687] {featur,
## perform,
## problem} => {propos} 0.1333333 0.8000000 1.600000 4
## [13688] {featur,
## propos,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [13689] {featur,
## model,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [13690] {data,
## propos,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [13691] {data,
## featur,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [13692] {featur,
## propos,
## problem} => {data} 0.1000000 0.7500000 1.730769 3
## [13693] {dataset,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [13694] {model,
## dataset,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [13695] {model,
## problem,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13696] {represent,
## problem,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [13697] {propos,
## problem,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13698] {represent,
## propos,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [13699] {show,
## problem,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13700] {propos,
## problem,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [13701] {show,
## propos,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [13702] {show,
## problem,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [13703] {model,
## problem,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [13704] {model,
## show,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [13705] {show,
## problem,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13706] {featur,
## problem,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [13707] {featur,
## show,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [13708] {propos,
## problem,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [13709] {model,
## problem,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13710] {model,
## propos,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [13711] {propos,
## problem,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13712] {featur,
## problem,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13713] {featur,
## propos,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [13714] {model,
## problem,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13715] {featur,
## problem,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [13716] {featur,
## model,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [13717] {represent,
## propos,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [13718] {featur,
## represent,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [13719] {featur,
## propos,
## problem} => {represent} 0.1000000 0.7500000 1.500000 3
## [13720] {paper,
## train,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [13721] {network,
## paper,
## result} => {train} 0.1000000 1.0000000 2.500000 3
## [13722] {network,
## paper,
## train} => {result} 0.1000000 0.7500000 2.250000 3
## [13723] {approach,
## method,
## paper} => {represent} 0.1000000 1.0000000 2.000000 3
## [13724] {method,
## paper,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [13725] {approach,
## paper,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [13726] {approach,
## method,
## represent} => {paper} 0.1000000 0.7500000 2.250000 3
## [13727] {method,
## paper,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [13728] {method,
## model,
## paper} => {show} 0.1000000 1.0000000 1.875000 3
## [13729] {paper,
## task,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [13730] {data,
## paper,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [13731] {data,
## paper,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [13732] {data,
## task,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [13733] {paper,
## task,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [13734] {paper,
## task,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [13735] {paper,
## train,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [13736] {task,
## train,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [13737] {paper,
## task,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [13738] {paper,
## represent,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [13739] {paper,
## represent,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [13740] {represent,
## task,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [13741] {paper,
## task,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [13742] {paper,
## show,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [13743] {paper,
## show,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [13744] {show,
## task,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [13745] {paper,
## task,
## train} => {featur} 0.1333333 1.0000000 1.875000 4
## [13746] {featur,
## paper,
## task} => {train} 0.1333333 1.0000000 2.500000 4
## [13747] {featur,
## paper,
## train} => {task} 0.1333333 0.8000000 2.181818 4
## [13748] {featur,
## task,
## train} => {paper} 0.1333333 1.0000000 3.000000 4
## [13749] {paper,
## task,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [13750] {network,
## paper,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [13751] {network,
## paper,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [13752] {network,
## task,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [13753] {data,
## paper,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [13754] {paper,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [13755] {data,
## paper,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [13756] {data,
## paper,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [13757] {featur,
## paper,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [13758] {data,
## featur,
## paper} => {task} 0.1000000 0.7500000 2.045455 3
## [13759] {data,
## paper,
## task} => {network} 0.1000000 0.7500000 1.184211 3
## [13760] {network,
## paper,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [13761] {data,
## network,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [13762] {paper,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [13763] {featur,
## paper,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [13764] {featur,
## paper,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [13765] {paper,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [13766] {featur,
## paper,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [13767] {featur,
## paper,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [13768] {paper,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [13769] {network,
## paper,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [13770] {network,
## paper,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [13771] {paper,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [13772] {featur,
## paper,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [13773] {featur,
## paper,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [13774] {featur,
## paper,
## task} => {network} 0.1000000 0.7500000 1.184211 3
## [13775] {network,
## paper,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [13776] {featur,
## network,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [13777] {data,
## paper,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [13778] {paper,
## train,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [13779] {data,
## paper,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [13780] {data,
## train,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [13781] {data,
## paper,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [13782] {paper,
## represent,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [13783] {data,
## paper,
## represent} => {train} 0.1000000 1.0000000 2.500000 3
## [13784] {data,
## paper,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [13785] {paper,
## show,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [13786] {data,
## paper,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [13787] {data,
## show,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [13788] {data,
## paper,
## train} => {model} 0.1000000 0.7500000 1.406250 3
## [13789] {model,
## paper,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [13790] {data,
## model,
## paper} => {train} 0.1000000 0.7500000 1.875000 3
## [13791] {data,
## model,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [13792] {data,
## paper,
## train} => {featur} 0.1333333 1.0000000 1.875000 4
## [13793] {featur,
## paper,
## train} => {data} 0.1333333 0.8000000 1.846154 4
## [13794] {data,
## featur,
## paper} => {train} 0.1333333 1.0000000 2.500000 4
## [13795] {data,
## featur,
## train} => {paper} 0.1333333 0.8000000 2.400000 4
## [13796] {paper,
## train,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13797] {paper,
## represent,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [13798] {paper,
## represent,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [13799] {represent,
## train,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [13800] {paper,
## train,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13801] {paper,
## train,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [13802] {paper,
## propos,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [13803] {train,
## propos,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [13804] {paper,
## train,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [13805] {featur,
## paper,
## train} => {learn} 0.1333333 0.8000000 1.846154 4
## [13806] {featur,
## paper,
## learn} => {train} 0.1333333 1.0000000 2.500000 4
## [13807] {featur,
## train,
## learn} => {paper} 0.1333333 1.0000000 3.000000 4
## [13808] {paper,
## represent,
## train} => {featur} 0.1333333 1.0000000 1.875000 4
## [13809] {featur,
## paper,
## train} => {represent} 0.1333333 0.8000000 1.600000 4
## [13810] {featur,
## paper,
## represent} => {train} 0.1333333 1.0000000 2.500000 4
## [13811] {featur,
## represent,
## train} => {paper} 0.1333333 0.8000000 2.400000 4
## [13812] {paper,
## represent,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [13813] {network,
## paper,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [13814] {network,
## paper,
## represent} => {train} 0.1000000 0.7500000 1.875000 3
## [13815] {paper,
## show,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [13816] {featur,
## paper,
## show} => {train} 0.1000000 1.0000000 2.500000 3
## [13817] {featur,
## show,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [13818] {paper,
## train,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [13819] {featur,
## paper,
## propos} => {train} 0.1000000 1.0000000 2.500000 3
## [13820] {featur,
## train,
## propos} => {paper} 0.1000000 1.0000000 3.000000 3
## [13821] {model,
## paper,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [13822] {featur,
## model,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [13823] {featur,
## model,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [13824] {network,
## paper,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [13825] {featur,
## network,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [13826] {featur,
## network,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [13827] {data,
## paper,
## perform} => {model} 0.1000000 1.0000000 1.875000 3
## [13828] {model,
## paper,
## perform} => {data} 0.1000000 0.7500000 1.730769 3
## [13829] {data,
## model,
## paper} => {perform} 0.1000000 0.7500000 1.607143 3
## [13830] {data,
## model,
## perform} => {paper} 0.1000000 0.7500000 2.250000 3
## [13831] {paper,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [13832] {paper,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [13833] {paper,
## propos,
## learn} => {perform} 0.1000000 0.7500000 1.607143 3
## [13834] {paper,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [13835] {model,
## paper,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [13836] {model,
## paper,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [13837] {paper,
## show,
## perform} => {model} 0.1000000 1.0000000 1.875000 3
## [13838] {model,
## paper,
## perform} => {show} 0.1000000 0.7500000 1.406250 3
## [13839] {paper,
## perform,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [13840] {model,
## paper,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [13841] {model,
## paper,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [13842] {data,
## paper,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [13843] {data,
## featur,
## paper} => {learn} 0.1000000 0.7500000 1.730769 3
## [13844] {featur,
## paper,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [13845] {data,
## paper,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [13846] {data,
## featur,
## paper} => {represent} 0.1000000 0.7500000 1.500000 3
## [13847] {featur,
## paper,
## represent} => {data} 0.1000000 0.7500000 1.730769 3
## [13848] {data,
## paper,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [13849] {data,
## model,
## paper} => {show} 0.1000000 0.7500000 1.406250 3
## [13850] {data,
## paper,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [13851] {data,
## featur,
## paper} => {show} 0.1000000 0.7500000 1.406250 3
## [13852] {featur,
## paper,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [13853] {data,
## model,
## paper} => {featur} 0.1000000 0.7500000 1.406250 3
## [13854] {data,
## featur,
## paper} => {model} 0.1000000 0.7500000 1.406250 3
## [13855] {featur,
## model,
## paper} => {data} 0.1000000 1.0000000 2.307692 3
## [13856] {paper,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [13857] {paper,
## represent,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [13858] {paper,
## represent,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13859] {paper,
## represent,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13860] {paper,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13861] {paper,
## represent,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [13862] {paper,
## represent,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13863] {featur,
## paper,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13864] {featur,
## paper,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [13865] {paper,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [13866] {model,
## paper,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [13867] {model,
## paper,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [13868] {paper,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13869] {featur,
## paper,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13870] {featur,
## paper,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [13871] {paper,
## represent,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [13872] {model,
## paper,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [13873] {paper,
## represent,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [13874] {network,
## paper,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [13875] {network,
## paper,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [13876] {featur,
## paper,
## represent} => {network} 0.1000000 0.7500000 1.184211 3
## [13877] {network,
## paper,
## represent} => {featur} 0.1000000 0.7500000 1.406250 3
## [13878] {featur,
## network,
## paper} => {represent} 0.1000000 1.0000000 2.000000 3
## [13879] {improv,
## perform,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [13880] {improv,
## propos,
## recognit} => {perform} 0.1000000 1.0000000 2.142857 3
## [13881] {perform,
## propos,
## recognit} => {improv} 0.1000000 0.7500000 2.500000 3
## [13882] {improv,
## perform,
## propos} => {recognit} 0.1000000 0.7500000 2.500000 3
## [13883] {task,
## recognit,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [13884] {data,
## recognit,
## result} => {task} 0.1000000 1.0000000 2.727273 3
## [13885] {data,
## task,
## recognit} => {result} 0.1000000 0.7500000 2.250000 3
## [13886] {data,
## task,
## result} => {recognit} 0.1000000 0.7500000 2.500000 3
## [13887] {task,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [13888] {recognit,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [13889] {task,
## recognit,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [13890] {task,
## recognit,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [13891] {represent,
## recognit,
## result} => {task} 0.1000000 1.0000000 2.727273 3
## [13892] {represent,
## task,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [13893] {task,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [13894] {show,
## recognit,
## result} => {task} 0.1000000 0.7500000 2.045455 3
## [13895] {show,
## task,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [13896] {show,
## task,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [13897] {data,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [13898] {recognit,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [13899] {data,
## result,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [13900] {data,
## recognit,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [13901] {represent,
## recognit,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [13902] {data,
## represent,
## recognit} => {result} 0.1000000 0.7500000 2.250000 3
## [13903] {data,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [13904] {show,
## recognit,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [13905] {data,
## show,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [13906] {data,
## show,
## result} => {recognit} 0.1000000 0.7500000 2.500000 3
## [13907] {dataset,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [13908] {show,
## recognit,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13909] {show,
## dataset,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [13910] {show,
## dataset,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [13911] {dataset,
## recognit,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [13912] {network,
## recognit,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13913] {network,
## dataset,
## recognit} => {result} 0.1000000 0.7500000 2.250000 3
## [13914] {recognit,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [13915] {represent,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [13916] {recognit,
## result,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [13917] {show,
## recognit,
## result} => {learn} 0.1000000 0.7500000 1.730769 3
## [13918] {show,
## recognit,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [13919] {show,
## result,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [13920] {represent,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [13921] {show,
## recognit,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [13922] {represent,
## show,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [13923] {show,
## recognit,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [13924] {featur,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [13925] {featur,
## show,
## recognit} => {result} 0.1000000 0.7500000 2.250000 3
## [13926] {featur,
## show,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [13927] {show,
## recognit,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [13928] {network,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [13929] {network,
## show,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [13930] {network,
## show,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [13931] {dataset,
## neural,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [13932] {neural,
## propos,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13933] {dataset,
## propos,
## recognit} => {neural} 0.1000000 0.7500000 2.250000 3
## [13934] {data,
## task,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13935] {task,
## dataset,
## recognit} => {data} 0.1000000 1.0000000 2.307692 3
## [13936] {data,
## dataset,
## recognit} => {task} 0.1000000 0.7500000 2.045455 3
## [13937] {data,
## task,
## recognit} => {learn} 0.1333333 1.0000000 2.307692 4
## [13938] {task,
## recognit,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [13939] {data,
## recognit,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [13940] {data,
## task,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [13941] {represent,
## task,
## recognit} => {data} 0.1000000 1.0000000 2.307692 3
## [13942] {data,
## represent,
## recognit} => {task} 0.1000000 0.7500000 2.045455 3
## [13943] {data,
## task,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [13944] {show,
## task,
## recognit} => {data} 0.1000000 1.0000000 2.307692 3
## [13945] {data,
## show,
## recognit} => {task} 0.1000000 1.0000000 2.727273 3
## [13946] {data,
## task,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [13947] {featur,
## task,
## recognit} => {data} 0.1000000 1.0000000 2.307692 3
## [13948] {data,
## featur,
## recognit} => {task} 0.1000000 0.7500000 2.045455 3
## [13949] {data,
## task,
## recognit} => {network} 0.1000000 0.7500000 1.184211 3
## [13950] {network,
## task,
## recognit} => {data} 0.1000000 1.0000000 2.307692 3
## [13951] {data,
## network,
## recognit} => {task} 0.1000000 1.0000000 2.727273 3
## [13952] {task,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [13953] {task,
## recognit,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13954] {dataset,
## recognit,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [13955] {task,
## dataset,
## recognit} => {network} 0.1000000 1.0000000 1.578947 3
## [13956] {network,
## task,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13957] {network,
## dataset,
## recognit} => {task} 0.1000000 0.7500000 2.045455 3
## [13958] {network,
## task,
## dataset} => {recognit} 0.1000000 0.7500000 2.500000 3
## [13959] {task,
## recognit,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13960] {represent,
## task,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [13961] {task,
## recognit,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [13962] {show,
## task,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [13963] {show,
## recognit,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [13964] {task,
## recognit,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13965] {featur,
## task,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [13966] {task,
## recognit,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [13967] {network,
## task,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [13968] {network,
## recognit,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [13969] {represent,
## task,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [13970] {show,
## task,
## recognit} => {represent} 0.1000000 1.0000000 2.000000 3
## [13971] {data,
## train,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13972] {train,
## dataset,
## recognit} => {data} 0.1000000 1.0000000 2.307692 3
## [13973] {data,
## dataset,
## recognit} => {train} 0.1000000 0.7500000 1.875000 3
## [13974] {data,
## train,
## dataset} => {recognit} 0.1000000 0.7500000 2.500000 3
## [13975] {data,
## train,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [13976] {train,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [13977] {data,
## train,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [13978] {data,
## train,
## recognit} => {represent} 0.1000000 1.0000000 2.000000 3
## [13979] {represent,
## train,
## recognit} => {data} 0.1000000 0.7500000 1.730769 3
## [13980] {data,
## represent,
## recognit} => {train} 0.1000000 0.7500000 1.875000 3
## [13981] {train,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [13982] {train,
## recognit,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13983] {dataset,
## recognit,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [13984] {train,
## dataset,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [13985] {train,
## dataset,
## recognit} => {represent} 0.1000000 1.0000000 2.000000 3
## [13986] {represent,
## train,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13987] {represent,
## dataset,
## recognit} => {train} 0.1000000 1.0000000 2.500000 3
## [13988] {represent,
## train,
## dataset} => {recognit} 0.1000000 0.7500000 2.500000 3
## [13989] {train,
## recognit,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [13990] {represent,
## train,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [13991] {represent,
## train,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [13992] {represent,
## train,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [13993] {show,
## train,
## recognit} => {represent} 0.1000000 1.0000000 2.000000 3
## [13994] {represent,
## show,
## train} => {recognit} 0.1000000 0.7500000 2.500000 3
## [13995] {represent,
## train,
## recognit} => {propos} 0.1000000 0.7500000 1.500000 3
## [13996] {train,
## propos,
## recognit} => {represent} 0.1000000 1.0000000 2.000000 3
## [13997] {represent,
## propos,
## recognit} => {train} 0.1000000 0.7500000 1.875000 3
## [13998] {represent,
## train,
## propos} => {recognit} 0.1000000 0.7500000 2.500000 3
## [13999] {represent,
## perform,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [14000] {perform,
## propos,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [14001] {represent,
## propos,
## recognit} => {perform} 0.1000000 0.7500000 1.607143 3
## [14002] {show,
## perform,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [14003] {perform,
## propos,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [14004] {show,
## propos,
## recognit} => {perform} 0.1000000 0.7500000 1.607143 3
## [14005] {perform,
## propos,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [14006] {featur,
## perform,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [14007] {featur,
## propos,
## recognit} => {perform} 0.1000000 0.7500000 1.607143 3
## [14008] {data,
## dataset,
## recognit} => {learn} 0.1333333 1.0000000 2.307692 4
## [14009] {data,
## recognit,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14010] {dataset,
## recognit,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [14011] {data,
## dataset,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [14012] {data,
## represent,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14013] {represent,
## dataset,
## recognit} => {data} 0.1000000 1.0000000 2.307692 3
## [14014] {data,
## dataset,
## recognit} => {propos} 0.1000000 0.7500000 1.500000 3
## [14015] {data,
## propos,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14016] {dataset,
## propos,
## recognit} => {data} 0.1000000 0.7500000 1.730769 3
## [14017] {data,
## dataset,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [14018] {data,
## featur,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14019] {featur,
## dataset,
## recognit} => {data} 0.1000000 0.7500000 1.730769 3
## [14020] {data,
## dataset,
## recognit} => {network} 0.1000000 0.7500000 1.184211 3
## [14021] {data,
## network,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14022] {network,
## dataset,
## recognit} => {data} 0.1000000 0.7500000 1.730769 3
## [14023] {data,
## recognit,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [14024] {data,
## represent,
## recognit} => {learn} 0.1333333 1.0000000 2.307692 4
## [14025] {represent,
## recognit,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [14026] {data,
## show,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [14027] {show,
## recognit,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [14028] {data,
## propos,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [14029] {propos,
## recognit,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [14030] {data,
## model,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [14031] {model,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [14032] {data,
## recognit,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [14033] {data,
## featur,
## recognit} => {learn} 0.1333333 1.0000000 2.307692 4
## [14034] {featur,
## recognit,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [14035] {data,
## network,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [14036] {network,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [14037] {data,
## network,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [14038] {data,
## represent,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [14039] {data,
## show,
## recognit} => {represent} 0.1000000 1.0000000 2.000000 3
## [14040] {data,
## represent,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [14041] {data,
## featur,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [14042] {featur,
## represent,
## recognit} => {data} 0.1000000 0.7500000 1.730769 3
## [14043] {data,
## model,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [14044] {data,
## featur,
## recognit} => {model} 0.1000000 0.7500000 1.406250 3
## [14045] {featur,
## model,
## recognit} => {data} 0.1000000 0.7500000 1.730769 3
## [14046] {dataset,
## recognit,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [14047] {represent,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [14048] {dataset,
## recognit,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [14049] {dataset,
## propos,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [14050] {propos,
## recognit,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14051] {dataset,
## recognit,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [14052] {featur,
## dataset,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [14053] {dataset,
## recognit,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [14054] {network,
## dataset,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [14055] {network,
## recognit,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14056] {network,
## dataset,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [14057] {show,
## dataset,
## recognit} => {network} 0.1000000 1.0000000 1.578947 3
## [14058] {network,
## dataset,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [14059] {network,
## show,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14060] {dataset,
## propos,
## recognit} => {model} 0.1000000 0.7500000 1.406250 3
## [14061] {model,
## dataset,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [14062] {model,
## propos,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14063] {dataset,
## propos,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [14064] {featur,
## dataset,
## recognit} => {propos} 0.1000000 0.7500000 1.500000 3
## [14065] {featur,
## propos,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14066] {dataset,
## propos,
## recognit} => {network} 0.1000000 0.7500000 1.184211 3
## [14067] {network,
## dataset,
## recognit} => {propos} 0.1000000 0.7500000 1.500000 3
## [14068] {network,
## propos,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14069] {model,
## dataset,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [14070] {featur,
## dataset,
## recognit} => {model} 0.1000000 0.7500000 1.406250 3
## [14071] {featur,
## model,
## recognit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14072] {featur,
## dataset,
## recognit} => {network} 0.1000000 0.7500000 1.184211 3
## [14073] {network,
## dataset,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [14074] {featur,
## network,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14075] {represent,
## recognit,
## learn} => {show} 0.1333333 0.8000000 1.500000 4
## [14076] {show,
## recognit,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [14077] {represent,
## show,
## recognit} => {learn} 0.1333333 0.8000000 1.846154 4
## [14078] {propos,
## recognit,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [14079] {represent,
## propos,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [14080] {represent,
## recognit,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [14081] {featur,
## recognit,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [14082] {featur,
## represent,
## recognit} => {learn} 0.1333333 1.0000000 2.307692 4
## [14083] {show,
## recognit,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [14084] {featur,
## show,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [14085] {propos,
## recognit,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [14086] {featur,
## propos,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [14087] {model,
## recognit,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [14088] {featur,
## model,
## recognit} => {learn} 0.1000000 0.7500000 1.730769 3
## [14089] {represent,
## propos,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [14090] {show,
## propos,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [14091] {featur,
## represent,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [14092] {featur,
## show,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [14093] {model,
## propos,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [14094] {featur,
## propos,
## recognit} => {model} 0.1000000 0.7500000 1.406250 3
## [14095] {featur,
## model,
## recognit} => {propos} 0.1000000 0.7500000 1.500000 3
## [14096] {improv,
## neural,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14097] {algorithm,
## improv,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [14098] {algorithm,
## improv,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [14099] {algorithm,
## neural,
## result} => {improv} 0.1000000 1.0000000 3.333333 3
## [14100] {improv,
## neural,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14101] {dataset,
## improv,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [14102] {dataset,
## neural,
## result} => {improv} 0.1000000 0.7500000 2.500000 3
## [14103] {improv,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14104] {network,
## improv,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [14105] {network,
## improv,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [14106] {algorithm,
## improv,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14107] {dataset,
## improv,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14108] {algorithm,
## dataset,
## improv} => {result} 0.1000000 0.7500000 2.250000 3
## [14109] {algorithm,
## dataset,
## result} => {improv} 0.1000000 1.0000000 3.333333 3
## [14110] {algorithm,
## improv,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14111] {network,
## improv,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14112] {network,
## algorithm,
## improv} => {result} 0.1000000 0.7500000 2.250000 3
## [14113] {network,
## algorithm,
## result} => {improv} 0.1000000 0.7500000 2.500000 3
## [14114] {dataset,
## improv,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14115] {network,
## improv,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14116] {network,
## dataset,
## improv} => {result} 0.1000000 0.7500000 2.250000 3
## [14117] {algorithm,
## improv,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [14118] {train,
## improv,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14119] {train,
## algorithm,
## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [14120] {train,
## algorithm,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [14121] {algorithm,
## improv,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [14122] {approach,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14123] {approach,
## algorithm,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [14124] {approach,
## algorithm,
## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [14125] {algorithm,
## improv,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [14126] {improv,
## neural,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14127] {algorithm,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [14128] {algorithm,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [14129] {algorithm,
## improv,
## neural} => {dataset} 0.1333333 1.0000000 2.307692 4
## [14130] {dataset,
## improv,
## neural} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [14131] {algorithm,
## dataset,
## improv} => {neural} 0.1333333 1.0000000 3.000000 4
## [14132] {algorithm,
## dataset,
## neural} => {improv} 0.1333333 1.0000000 3.333333 4
## [14133] {algorithm,
## improv,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [14134] {improv,
## neural,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14135] {algorithm,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [14136] {algorithm,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [14137] {algorithm,
## improv,
## neural} => {network} 0.1333333 1.0000000 1.578947 4
## [14138] {network,
## improv,
## neural} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [14139] {network,
## algorithm,
## improv} => {neural} 0.1333333 1.0000000 3.000000 4
## [14140] {network,
## algorithm,
## neural} => {improv} 0.1333333 0.8000000 2.666667 4
## [14141] {train,
## improv,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [14142] {improv,
## neural,
## perform} => {train} 0.1000000 0.7500000 1.875000 3
## [14143] {train,
## improv,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [14144] {train,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [14145] {train,
## improv,
## neural} => {dataset} 0.1333333 1.0000000 2.307692 4
## [14146] {dataset,
## improv,
## neural} => {train} 0.1333333 0.8000000 2.000000 4
## [14147] {train,
## dataset,
## improv} => {neural} 0.1333333 1.0000000 3.000000 4
## [14148] {train,
## dataset,
## neural} => {improv} 0.1333333 0.8000000 2.666667 4
## [14149] {train,
## improv,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [14150] {improv,
## neural,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [14151] {train,
## improv,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [14152] {train,
## improv,
## neural} => {network} 0.1000000 0.7500000 1.184211 3
## [14153] {network,
## improv,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [14154] {network,
## train,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [14155] {approach,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14156] {approach,
## dataset,
## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [14157] {approach,
## dataset,
## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [14158] {approach,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [14159] {improv,
## neural,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [14160] {approach,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [14161] {approach,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14162] {network,
## improv,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [14163] {approach,
## network,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [14164] {improv,
## neural,
## perform} => {dataset} 0.1333333 1.0000000 2.307692 4
## [14165] {dataset,
## improv,
## neural} => {perform} 0.1333333 0.8000000 1.714286 4
## [14166] {dataset,
## improv,
## perform} => {neural} 0.1333333 0.8000000 2.400000 4
## [14167] {dataset,
## neural,
## perform} => {improv} 0.1333333 1.0000000 3.333333 4
## [14168] {improv,
## neural,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [14169] {improv,
## neural,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [14170] {improv,
## perform,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [14171] {neural,
## perform,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [14172] {improv,
## neural,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [14173] {featur,
## improv,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [14174] {featur,
## improv,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [14175] {featur,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [14176] {improv,
## neural,
## perform} => {network} 0.1000000 0.7500000 1.184211 3
## [14177] {network,
## improv,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [14178] {network,
## improv,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [14179] {network,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [14180] {dataset,
## improv,
## neural} => {propos} 0.1333333 0.8000000 1.600000 4
## [14181] {improv,
## neural,
## propos} => {dataset} 0.1333333 1.0000000 2.307692 4
## [14182] {dataset,
## improv,
## propos} => {neural} 0.1333333 1.0000000 3.000000 4
## [14183] {dataset,
## neural,
## propos} => {improv} 0.1333333 0.8000000 2.666667 4
## [14184] {model,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14185] {model,
## dataset,
## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [14186] {model,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [14187] {featur,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14188] {featur,
## dataset,
## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [14189] {featur,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [14190] {dataset,
## improv,
## neural} => {network} 0.1333333 0.8000000 1.263158 4
## [14191] {network,
## improv,
## neural} => {dataset} 0.1333333 1.0000000 2.307692 4
## [14192] {network,
## dataset,
## improv} => {neural} 0.1333333 1.0000000 3.000000 4
## [14193] {network,
## dataset,
## neural} => {improv} 0.1333333 0.8000000 2.666667 4
## [14194] {improv,
## neural,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [14195] {model,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [14196] {model,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [14197] {model,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [14198] {improv,
## neural,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [14199] {network,
## improv,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [14200] {network,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [14201] {method,
## algorithm,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [14202] {method,
## show,
## improv} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14203] {show,
## algorithm,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [14204] {method,
## show,
## algorithm} => {improv} 0.1000000 0.7500000 2.500000 3
## [14205] {approach,
## method,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [14206] {method,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [14207] {approach,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [14208] {approach,
## method,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [14209] {approach,
## method,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14210] {method,
## dataset,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [14211] {approach,
## dataset,
## improv} => {method} 0.1000000 0.7500000 2.045455 3
## [14212] {approach,
## method,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [14213] {method,
## show,
## improv} => {approach} 0.1000000 0.7500000 1.875000 3
## [14214] {approach,
## show,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [14215] {method,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14216] {method,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [14217] {method,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [14218] {method,
## show,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [14219] {show,
## improv,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [14220] {method,
## dataset,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [14221] {method,
## show,
## improv} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14222] {show,
## dataset,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [14223] {method,
## show,
## improv} => {model} 0.1000000 0.7500000 1.406250 3
## [14224] {method,
## model,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [14225] {model,
## show,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [14226] {train,
## algorithm,
## improv} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14227] {algorithm,
## dataset,
## improv} => {train} 0.1000000 0.7500000 1.875000 3
## [14228] {train,
## dataset,
## improv} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14229] {train,
## algorithm,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [14230] {train,
## algorithm,
## improv} => {network} 0.1000000 0.7500000 1.184211 3
## [14231] {network,
## algorithm,
## improv} => {train} 0.1000000 0.7500000 1.875000 3
## [14232] {network,
## train,
## improv} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14233] {network,
## train,
## algorithm} => {improv} 0.1000000 0.7500000 2.500000 3
## [14234] {approach,
## algorithm,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14235] {algorithm,
## dataset,
## improv} => {approach} 0.1000000 0.7500000 1.875000 3
## [14236] {approach,
## dataset,
## improv} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14237] {approach,
## algorithm,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [14238] {approach,
## algorithm,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [14239] {algorithm,
## improv,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [14240] {approach,
## improv,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14241] {approach,
## algorithm,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [14242] {approach,
## algorithm,
## improv} => {network} 0.1000000 1.0000000 1.578947 3
## [14243] {network,
## algorithm,
## improv} => {approach} 0.1000000 0.7500000 1.875000 3
## [14244] {approach,
## network,
## improv} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14245] {approach,
## network,
## algorithm} => {improv} 0.1000000 0.7500000 2.500000 3
## [14246] {algorithm,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14247] {algorithm,
## dataset,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [14248] {algorithm,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [14249] {algorithm,
## improv,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [14250] {network,
## algorithm,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [14251] {network,
## improv,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14252] {network,
## algorithm,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [14253] {algorithm,
## dataset,
## improv} => {propos} 0.1000000 0.7500000 1.500000 3
## [14254] {algorithm,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14255] {dataset,
## improv,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14256] {algorithm,
## dataset,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [14257] {algorithm,
## dataset,
## improv} => {network} 0.1333333 1.0000000 1.578947 4
## [14258] {network,
## algorithm,
## improv} => {dataset} 0.1333333 1.0000000 2.307692 4
## [14259] {network,
## dataset,
## improv} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [14260] {network,
## algorithm,
## dataset} => {improv} 0.1333333 1.0000000 3.333333 4
## [14261] {algorithm,
## improv,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [14262] {network,
## algorithm,
## improv} => {propos} 0.1000000 0.7500000 1.500000 3
## [14263] {network,
## improv,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14264] {network,
## algorithm,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [14265] {train,
## improv,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [14266] {train,
## improv,
## perform} => {work} 0.1000000 0.7500000 1.875000 3
## [14267] {improv,
## perform,
## work} => {train} 0.1000000 0.7500000 1.875000 3
## [14268] {train,
## perform,
## work} => {improv} 0.1000000 1.0000000 3.333333 3
## [14269] {train,
## improv,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14270] {train,
## dataset,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [14271] {train,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [14272] {train,
## improv,
## perform} => {represent} 0.1000000 0.7500000 1.500000 3
## [14273] {represent,
## train,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [14274] {represent,
## improv,
## perform} => {train} 0.1000000 1.0000000 2.500000 3
## [14275] {represent,
## train,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [14276] {train,
## improv,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [14277] {train,
## improv,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [14278] {improv,
## perform,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [14279] {train,
## perform,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [14280] {train,
## dataset,
## improv} => {propos} 0.1000000 0.7500000 1.500000 3
## [14281] {train,
## improv,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14282] {dataset,
## improv,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [14283] {train,
## dataset,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [14284] {train,
## dataset,
## improv} => {network} 0.1000000 0.7500000 1.184211 3
## [14285] {network,
## train,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14286] {network,
## dataset,
## improv} => {train} 0.1000000 0.7500000 1.875000 3
## [14287] {approach,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14288] {approach,
## dataset,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [14289] {approach,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [14290] {approach,
## show,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [14291] {show,
## improv,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [14292] {approach,
## show,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [14293] {approach,
## dataset,
## improv} => {show} 0.1000000 0.7500000 1.406250 3
## [14294] {approach,
## show,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14295] {show,
## dataset,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [14296] {approach,
## dataset,
## improv} => {propos} 0.1000000 0.7500000 1.500000 3
## [14297] {approach,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14298] {dataset,
## improv,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [14299] {approach,
## dataset,
## improv} => {model} 0.1000000 0.7500000 1.406250 3
## [14300] {approach,
## model,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14301] {model,
## dataset,
## improv} => {approach} 0.1000000 0.7500000 1.875000 3
## [14302] {approach,
## dataset,
## improv} => {network} 0.1000000 0.7500000 1.184211 3
## [14303] {approach,
## network,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14304] {network,
## dataset,
## improv} => {approach} 0.1000000 0.7500000 1.875000 3
## [14305] {approach,
## improv,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [14306] {approach,
## network,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [14307] {network,
## improv,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [14308] {improv,
## perform,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [14309] {network,
## improv,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [14310] {network,
## improv,
## perform} => {work} 0.1000000 0.7500000 1.875000 3
## [14311] {network,
## perform,
## work} => {improv} 0.1000000 0.7500000 2.500000 3
## [14312] {show,
## improv,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14313] {show,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [14314] {show,
## dataset,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [14315] {improv,
## perform,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14316] {dataset,
## improv,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [14317] {model,
## improv,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14318] {model,
## dataset,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [14319] {dataset,
## improv,
## perform} => {featur} 0.1333333 0.8000000 1.500000 4
## [14320] {featur,
## improv,
## perform} => {dataset} 0.1333333 1.0000000 2.307692 4
## [14321] {featur,
## dataset,
## improv} => {perform} 0.1333333 1.0000000 2.142857 4
## [14322] {network,
## improv,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14323] {network,
## dataset,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [14324] {network,
## dataset,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [14325] {show,
## improv,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [14326] {improv,
## perform,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [14327] {show,
## improv,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [14328] {model,
## improv,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [14329] {featur,
## improv,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [14330] {featur,
## model,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [14331] {dataset,
## improv,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [14332] {model,
## dataset,
## improv} => {propos} 0.1000000 0.7500000 1.500000 3
## [14333] {model,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14334] {dataset,
## improv,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [14335] {network,
## dataset,
## improv} => {propos} 0.1000000 0.7500000 1.500000 3
## [14336] {network,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14337] {model,
## dataset,
## improv} => {featur} 0.1000000 0.7500000 1.406250 3
## [14338] {featur,
## dataset,
## improv} => {model} 0.1000000 0.7500000 1.406250 3
## [14339] {featur,
## model,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14340] {algorithm,
## neural,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14341] {dataset,
## neural,
## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14342] {algorithm,
## dataset,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [14343] {algorithm,
## dataset,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [14344] {algorithm,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14345] {network,
## algorithm,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [14346] {train,
## neural,
## result} => {approach} 0.1000000 0.7500000 1.875000 3
## [14347] {approach,
## neural,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [14348] {approach,
## train,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [14349] {approach,
## train,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [14350] {train,
## neural,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14351] {dataset,
## neural,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [14352] {train,
## dataset,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [14353] {train,
## neural,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [14354] {represent,
## neural,
## result} => {train} 0.1000000 1.0000000 2.500000 3
## [14355] {represent,
## train,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [14356] {represent,
## train,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [14357] {train,
## neural,
## result} => {propos} 0.1000000 0.7500000 1.500000 3
## [14358] {neural,
## propos,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [14359] {train,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [14360] {train,
## neural,
## result} => {network} 0.1333333 1.0000000 1.578947 4
## [14361] {network,
## neural,
## result} => {train} 0.1333333 0.8000000 2.000000 4
## [14362] {network,
## train,
## neural} => {result} 0.1333333 0.8000000 2.400000 4
## [14363] {approach,
## neural,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14364] {dataset,
## neural,
## result} => {approach} 0.1000000 0.7500000 1.875000 3
## [14365] {approach,
## dataset,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [14366] {approach,
## dataset,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [14367] {approach,
## neural,
## result} => {propos} 0.1333333 1.0000000 2.000000 4
## [14368] {neural,
## propos,
## result} => {approach} 0.1333333 1.0000000 2.500000 4
## [14369] {approach,
## propos,
## result} => {neural} 0.1333333 0.8000000 2.400000 4
## [14370] {approach,
## neural,
## propos} => {result} 0.1333333 0.8000000 2.400000 4
## [14371] {approach,
## neural,
## result} => {network} 0.1333333 1.0000000 1.578947 4
## [14372] {network,
## neural,
## result} => {approach} 0.1333333 0.8000000 2.000000 4
## [14373] {approach,
## network,
## result} => {neural} 0.1333333 1.0000000 3.000000 4
## [14374] {dataset,
## neural,
## result} => {propos} 0.1000000 0.7500000 1.500000 3
## [14375] {neural,
## propos,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14376] {dataset,
## propos,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [14377] {dataset,
## neural,
## result} => {network} 0.1333333 1.0000000 1.578947 4
## [14378] {network,
## neural,
## result} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14379] {network,
## dataset,
## result} => {neural} 0.1333333 0.8000000 2.400000 4
## [14380] {network,
## dataset,
## neural} => {result} 0.1333333 0.8000000 2.400000 4
## [14381] {represent,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14382] {network,
## represent,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [14383] {neural,
## propos,
## result} => {network} 0.1333333 1.0000000 1.578947 4
## [14384] {network,
## neural,
## result} => {propos} 0.1333333 0.8000000 1.600000 4
## [14385] {network,
## propos,
## result} => {neural} 0.1333333 1.0000000 3.000000 4
## [14386] {network,
## neural,
## propos} => {result} 0.1333333 0.8000000 2.400000 4
## [14387] {featur,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14388] {featur,
## network,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [14389] {featur,
## network,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [14390] {train,
## algorithm,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14391] {network,
## algorithm,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [14392] {network,
## train,
## algorithm} => {result} 0.1000000 0.7500000 2.250000 3
## [14393] {algorithm,
## dataset,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14394] {network,
## algorithm,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14395] {network,
## algorithm,
## dataset} => {result} 0.1000000 0.7500000 2.250000 3
## [14396] {show,
## algorithm,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [14397] {model,
## algorithm,
## result} => {show} 0.1000000 0.7500000 1.406250 3
## [14398] {model,
## show,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14399] {task,
## train,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [14400] {train,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [14401] {task,
## train,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [14402] {task,
## train,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [14403] {represent,
## train,
## result} => {task} 0.1000000 0.7500000 2.045455 3
## [14404] {represent,
## task,
## train} => {result} 0.1000000 0.7500000 2.250000 3
## [14405] {task,
## train,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14406] {network,
## task,
## result} => {train} 0.1000000 1.0000000 2.500000 3
## [14407] {network,
## task,
## train} => {result} 0.1000000 0.7500000 2.250000 3
## [14408] {approach,
## task,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [14409] {approach,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [14410] {approach,
## task,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [14411] {approach,
## task,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [14412] {approach,
## represent,
## result} => {task} 0.1000000 1.0000000 2.727273 3
## [14413] {approach,
## task,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [14414] {task,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [14415] {data,
## task,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14416] {task,
## dataset,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [14417] {data,
## dataset,
## result} => {task} 0.1000000 0.7500000 2.045455 3
## [14418] {data,
## task,
## result} => {learn} 0.1333333 1.0000000 2.307692 4
## [14419] {task,
## result,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [14420] {data,
## result,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [14421] {data,
## task,
## result} => {represent} 0.1333333 1.0000000 2.000000 4
## [14422] {represent,
## task,
## result} => {data} 0.1333333 0.8000000 1.846154 4
## [14423] {data,
## represent,
## result} => {task} 0.1333333 0.8000000 2.181818 4
## [14424] {data,
## task,
## result} => {show} 0.1000000 0.7500000 1.406250 3
## [14425] {show,
## task,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [14426] {data,
## show,
## result} => {task} 0.1000000 0.7500000 2.045455 3
## [14427] {data,
## task,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [14428] {featur,
## task,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [14429] {data,
## featur,
## result} => {task} 0.1000000 0.7500000 2.045455 3
## [14430] {task,
## dataset,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [14431] {dataset,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [14432] {task,
## dataset,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [14433] {represent,
## dataset,
## result} => {task} 0.1000000 0.7500000 2.045455 3
## [14434] {represent,
## task,
## dataset} => {result} 0.1000000 0.7500000 2.250000 3
## [14435] {task,
## result,
## learn} => {represent} 0.1666667 1.0000000 2.000000 5
## [14436] {represent,
## task,
## result} => {learn} 0.1666667 1.0000000 2.307692 5
## [14437] {represent,
## result,
## learn} => {task} 0.1666667 1.0000000 2.727273 5
## [14438] {represent,
## task,
## learn} => {result} 0.1666667 0.8333333 2.500000 5
## [14439] {show,
## task,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [14440] {show,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [14441] {task,
## propos,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [14442] {propos,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [14443] {task,
## result,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [14444] {featur,
## task,
## result} => {learn} 0.1333333 1.0000000 2.307692 4
## [14445] {featur,
## result,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [14446] {network,
## task,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [14447] {network,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [14448] {show,
## task,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [14449] {represent,
## show,
## result} => {task} 0.1000000 1.0000000 2.727273 3
## [14450] {task,
## propos,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [14451] {represent,
## propos,
## result} => {task} 0.1000000 1.0000000 2.727273 3
## [14452] {represent,
## task,
## result} => {featur} 0.1333333 0.8000000 1.500000 4
## [14453] {featur,
## task,
## result} => {represent} 0.1333333 1.0000000 2.000000 4
## [14454] {featur,
## represent,
## result} => {task} 0.1333333 0.8000000 2.181818 4
## [14455] {network,
## task,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [14456] {network,
## represent,
## result} => {task} 0.1000000 0.7500000 2.045455 3
## [14457] {approach,
## train,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [14458] {train,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [14459] {approach,
## train,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [14460] {approach,
## train,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14461] {approach,
## network,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [14462] {train,
## result,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [14463] {network,
## result,
## work} => {train} 0.1000000 1.0000000 2.500000 3
## [14464] {network,
## train,
## work} => {result} 0.1000000 0.7500000 2.250000 3
## [14465] {data,
## train,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14466] {train,
## dataset,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [14467] {data,
## dataset,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [14468] {data,
## train,
## dataset} => {result} 0.1000000 0.7500000 2.250000 3
## [14469] {data,
## train,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [14470] {represent,
## train,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [14471] {data,
## train,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14472] {data,
## network,
## result} => {train} 0.1000000 1.0000000 2.500000 3
## [14473] {data,
## network,
## train} => {result} 0.1000000 0.7500000 2.250000 3
## [14474] {train,
## dataset,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [14475] {represent,
## train,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14476] {represent,
## dataset,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [14477] {represent,
## train,
## dataset} => {result} 0.1000000 0.7500000 2.250000 3
## [14478] {train,
## dataset,
## result} => {network} 0.1333333 1.0000000 1.578947 4
## [14479] {network,
## dataset,
## result} => {train} 0.1333333 0.8000000 2.000000 4
## [14480] {network,
## train,
## dataset} => {result} 0.1333333 0.8000000 2.400000 4
## [14481] {train,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [14482] {represent,
## train,
## result} => {learn} 0.1000000 0.7500000 1.730769 3
## [14483] {represent,
## train,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [14484] {train,
## result,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [14485] {network,
## result,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [14486] {network,
## train,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [14487] {represent,
## train,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [14488] {featur,
## train,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [14489] {represent,
## train,
## result} => {network} 0.1333333 1.0000000 1.578947 4
## [14490] {network,
## represent,
## result} => {train} 0.1333333 1.0000000 2.500000 4
## [14491] {network,
## represent,
## train} => {result} 0.1333333 0.8000000 2.400000 4
## [14492] {train,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14493] {network,
## propos,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [14494] {network,
## train,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [14495] {featur,
## train,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14496] {featur,
## network,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [14497] {featur,
## network,
## train} => {result} 0.1000000 0.7500000 2.250000 3
## [14498] {approach,
## dataset,
## result} => {propos} 0.1333333 1.0000000 2.000000 4
## [14499] {approach,
## propos,
## result} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14500] {dataset,
## propos,
## result} => {approach} 0.1333333 1.0000000 2.500000 4
## [14501] {approach,
## dataset,
## result} => {model} 0.1000000 0.7500000 1.406250 3
## [14502] {approach,
## model,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14503] {model,
## dataset,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [14504] {approach,
## dataset,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [14505] {approach,
## network,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14506] {approach,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [14507] {approach,
## represent,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [14508] {approach,
## result,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [14509] {propos,
## result,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [14510] {approach,
## represent,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [14511] {represent,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [14512] {approach,
## model,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [14513] {model,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [14514] {approach,
## featur,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [14515] {featur,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [14516] {approach,
## propos,
## result} => {network} 0.1333333 0.8000000 1.263158 4
## [14517] {approach,
## network,
## result} => {propos} 0.1333333 1.0000000 2.000000 4
## [14518] {network,
## propos,
## result} => {approach} 0.1333333 1.0000000 2.500000 4
## [14519] {data,
## result,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14520] {dataset,
## result,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [14521] {data,
## dataset,
## result} => {work} 0.1000000 0.7500000 1.875000 3
## [14522] {data,
## result,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [14523] {represent,
## result,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [14524] {data,
## represent,
## work} => {result} 0.1000000 0.7500000 2.250000 3
## [14525] {dataset,
## result,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [14526] {represent,
## result,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14527] {represent,
## dataset,
## result} => {work} 0.1000000 0.7500000 1.875000 3
## [14528] {represent,
## dataset,
## work} => {result} 0.1000000 0.7500000 2.250000 3
## [14529] {dataset,
## perform,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [14530] {featur,
## perform,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14531] {featur,
## dataset,
## result} => {perform} 0.1000000 0.7500000 1.607143 3
## [14532] {data,
## dataset,
## result} => {learn} 0.1000000 0.7500000 1.730769 3
## [14533] {data,
## result,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14534] {dataset,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [14535] {data,
## dataset,
## result} => {represent} 0.1333333 1.0000000 2.000000 4
## [14536] {data,
## represent,
## result} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14537] {represent,
## dataset,
## result} => {data} 0.1333333 1.0000000 2.307692 4
## [14538] {data,
## dataset,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [14539] {data,
## featur,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14540] {featur,
## dataset,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [14541] {data,
## dataset,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [14542] {data,
## network,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14543] {data,
## result,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [14544] {data,
## represent,
## result} => {learn} 0.1333333 0.8000000 1.846154 4
## [14545] {represent,
## result,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [14546] {data,
## result,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [14547] {data,
## show,
## result} => {learn} 0.1000000 0.7500000 1.730769 3
## [14548] {show,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [14549] {data,
## result,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [14550] {data,
## featur,
## result} => {learn} 0.1000000 0.7500000 1.730769 3
## [14551] {featur,
## result,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [14552] {data,
## show,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [14553] {represent,
## show,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [14554] {data,
## represent,
## result} => {featur} 0.1333333 0.8000000 1.500000 4
## [14555] {data,
## featur,
## result} => {represent} 0.1333333 1.0000000 2.000000 4
## [14556] {featur,
## represent,
## result} => {data} 0.1333333 0.8000000 1.846154 4
## [14557] {data,
## network,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [14558] {network,
## represent,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [14559] {dataset,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [14560] {represent,
## dataset,
## result} => {learn} 0.1000000 0.7500000 1.730769 3
## [14561] {represent,
## dataset,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [14562] {featur,
## dataset,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [14563] {represent,
## dataset,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [14564] {network,
## represent,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14565] {network,
## represent,
## dataset} => {result} 0.1000000 0.7500000 2.250000 3
## [14566] {show,
## dataset,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [14567] {network,
## show,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14568] {dataset,
## propos,
## result} => {model} 0.1000000 0.7500000 1.406250 3
## [14569] {model,
## dataset,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [14570] {model,
## propos,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14571] {dataset,
## propos,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [14572] {network,
## propos,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14573] {featur,
## dataset,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [14574] {featur,
## network,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14575] {show,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [14576] {represent,
## show,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [14577] {propos,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [14578] {represent,
## propos,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [14579] {represent,
## result,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [14580] {featur,
## result,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [14581] {featur,
## represent,
## result} => {learn} 0.1333333 0.8000000 1.846154 4
## [14582] {network,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [14583] {network,
## represent,
## result} => {learn} 0.1000000 0.7500000 1.730769 3
## [14584] {network,
## represent,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [14585] {featur,
## network,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [14586] {approach,
## method,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [14587] {method,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [14588] {approach,
## method,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14589] {method,
## network,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [14590] {method,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [14591] {method,
## network,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [14592] {train,
## algorithm,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14593] {algorithm,
## dataset,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [14594] {train,
## algorithm,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [14595] {train,
## algorithm,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14596] {network,
## train,
## algorithm} => {neural} 0.1000000 0.7500000 2.250000 3
## [14597] {approach,
## algorithm,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14598] {algorithm,
## dataset,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [14599] {approach,
## dataset,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14600] {approach,
## algorithm,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [14601] {approach,
## algorithm,
## neural} => {show} 0.1000000 0.7500000 1.406250 3
## [14602] {show,
## algorithm,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [14603] {approach,
## show,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14604] {approach,
## show,
## algorithm} => {neural} 0.1000000 1.0000000 3.000000 3
## [14605] {approach,
## algorithm,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [14606] {algorithm,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [14607] {approach,
## algorithm,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [14608] {approach,
## algorithm,
## neural} => {model} 0.1000000 0.7500000 1.406250 3
## [14609] {model,
## algorithm,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [14610] {approach,
## model,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14611] {approach,
## model,
## algorithm} => {neural} 0.1000000 1.0000000 3.000000 3
## [14612] {approach,
## algorithm,
## neural} => {network} 0.1333333 1.0000000 1.578947 4
## [14613] {network,
## algorithm,
## neural} => {approach} 0.1333333 0.8000000 2.000000 4
## [14614] {approach,
## network,
## algorithm} => {neural} 0.1333333 1.0000000 3.000000 4
## [14615] {algorithm,
## neural,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [14616] {network,
## algorithm,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [14617] {algorithm,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14618] {algorithm,
## dataset,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [14619] {dataset,
## neural,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14620] {algorithm,
## dataset,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [14621] {algorithm,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [14622] {network,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14623] {network,
## algorithm,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [14624] {algorithm,
## dataset,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [14625] {algorithm,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14626] {algorithm,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [14627] {algorithm,
## dataset,
## neural} => {network} 0.1333333 1.0000000 1.578947 4
## [14628] {network,
## algorithm,
## neural} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14629] {network,
## dataset,
## neural} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [14630] {network,
## algorithm,
## dataset} => {neural} 0.1333333 1.0000000 3.000000 4
## [14631] {show,
## algorithm,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14632] {network,
## show,
## algorithm} => {neural} 0.1000000 1.0000000 3.000000 3
## [14633] {algorithm,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [14634] {network,
## algorithm,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [14635] {model,
## algorithm,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14636] {model,
## network,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14637] {model,
## network,
## algorithm} => {neural} 0.1000000 1.0000000 3.000000 3
## [14638] {featur,
## algorithm,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14639] {featur,
## network,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14640] {featur,
## network,
## algorithm} => {neural} 0.1000000 1.0000000 3.000000 3
## [14641] {approach,
## train,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14642] {approach,
## dataset,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [14643] {approach,
## train,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [14644] {approach,
## train,
## neural} => {propos} 0.1333333 1.0000000 2.000000 4
## [14645] {train,
## neural,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [14646] {approach,
## neural,
## propos} => {train} 0.1333333 0.8000000 2.000000 4
## [14647] {approach,
## train,
## propos} => {neural} 0.1333333 1.0000000 3.000000 4
## [14648] {approach,
## train,
## neural} => {network} 0.1333333 1.0000000 1.578947 4
## [14649] {network,
## train,
## neural} => {approach} 0.1333333 0.8000000 2.000000 4
## [14650] {approach,
## network,
## train} => {neural} 0.1333333 0.8000000 2.400000 4
## [14651] {train,
## neural,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14652] {dataset,
## neural,
## work} => {train} 0.1000000 1.0000000 2.500000 3
## [14653] {train,
## dataset,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [14654] {train,
## neural,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [14655] {network,
## train,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [14656] {train,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14657] {dataset,
## neural,
## perform} => {train} 0.1000000 0.7500000 1.875000 3
## [14658] {train,
## dataset,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [14659] {data,
## train,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14660] {data,
## dataset,
## neural} => {train} 0.1000000 1.0000000 2.500000 3
## [14661] {data,
## train,
## dataset} => {neural} 0.1000000 0.7500000 2.250000 3
## [14662] {data,
## train,
## neural} => {represent} 0.1000000 1.0000000 2.000000 3
## [14663] {represent,
## train,
## neural} => {data} 0.1000000 0.7500000 1.730769 3
## [14664] {data,
## represent,
## neural} => {train} 0.1000000 1.0000000 2.500000 3
## [14665] {represent,
## train,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14666] {represent,
## dataset,
## neural} => {train} 0.1000000 1.0000000 2.500000 3
## [14667] {represent,
## train,
## dataset} => {neural} 0.1000000 0.7500000 2.250000 3
## [14668] {train,
## dataset,
## neural} => {propos} 0.1333333 0.8000000 1.600000 4
## [14669] {train,
## neural,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14670] {dataset,
## neural,
## propos} => {train} 0.1333333 0.8000000 2.000000 4
## [14671] {train,
## dataset,
## propos} => {neural} 0.1333333 1.0000000 3.000000 4
## [14672] {train,
## dataset,
## neural} => {network} 0.1333333 0.8000000 1.263158 4
## [14673] {network,
## train,
## neural} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14674] {network,
## dataset,
## neural} => {train} 0.1333333 0.8000000 2.000000 4
## [14675] {network,
## train,
## dataset} => {neural} 0.1333333 0.8000000 2.400000 4
## [14676] {train,
## neural,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [14677] {represent,
## train,
## neural} => {learn} 0.1000000 0.7500000 1.730769 3
## [14678] {represent,
## neural,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [14679] {represent,
## train,
## learn} => {neural} 0.1000000 0.7500000 2.250000 3
## [14680] {train,
## neural,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [14681] {neural,
## propos,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [14682] {train,
## propos,
## learn} => {neural} 0.1000000 0.7500000 2.250000 3
## [14683] {represent,
## train,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [14684] {represent,
## neural,
## propos} => {train} 0.1000000 1.0000000 2.500000 3
## [14685] {represent,
## train,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [14686] {represent,
## train,
## neural} => {featur} 0.1000000 0.7500000 1.406250 3
## [14687] {featur,
## train,
## neural} => {represent} 0.1000000 1.0000000 2.000000 3
## [14688] {featur,
## represent,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [14689] {represent,
## train,
## neural} => {network} 0.1000000 0.7500000 1.184211 3
## [14690] {train,
## neural,
## propos} => {network} 0.1333333 0.8000000 1.263158 4
## [14691] {network,
## train,
## neural} => {propos} 0.1333333 0.8000000 1.600000 4
## [14692] {network,
## neural,
## propos} => {train} 0.1333333 0.8000000 2.000000 4
## [14693] {network,
## train,
## propos} => {neural} 0.1333333 1.0000000 3.000000 4
## [14694] {approach,
## neural,
## work} => {show} 0.1000000 1.0000000 1.875000 3
## [14695] {approach,
## show,
## neural} => {work} 0.1000000 0.7500000 1.875000 3
## [14696] {show,
## neural,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [14697] {approach,
## show,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [14698] {approach,
## neural,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [14699] {approach,
## network,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [14700] {approach,
## dataset,
## neural} => {show} 0.1000000 0.7500000 1.406250 3
## [14701] {approach,
## show,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14702] {show,
## dataset,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [14703] {approach,
## dataset,
## neural} => {propos} 0.1333333 1.0000000 2.000000 4
## [14704] {approach,
## neural,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14705] {dataset,
## neural,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [14706] {approach,
## dataset,
## neural} => {network} 0.1333333 1.0000000 1.578947 4
## [14707] {network,
## dataset,
## neural} => {approach} 0.1333333 0.8000000 2.000000 4
## [14708] {approach,
## network,
## dataset} => {neural} 0.1333333 0.8000000 2.400000 4
## [14709] {approach,
## neural,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [14710] {approach,
## represent,
## neural} => {learn} 0.1000000 1.0000000 2.307692 3
## [14711] {represent,
## neural,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [14712] {approach,
## neural,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [14713] {network,
## neural,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [14714] {approach,
## network,
## learn} => {neural} 0.1000000 0.7500000 2.250000 3
## [14715] {approach,
## represent,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14716] {approach,
## show,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [14717] {show,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [14718] {approach,
## show,
## neural} => {network} 0.1333333 1.0000000 1.578947 4
## [14719] {network,
## show,
## neural} => {approach} 0.1333333 0.8000000 2.000000 4
## [14720] {approach,
## neural,
## propos} => {network} 0.1666667 1.0000000 1.578947 5
## [14721] {approach,
## network,
## neural} => {propos} 0.1666667 0.8333333 1.666667 5
## [14722] {network,
## neural,
## propos} => {approach} 0.1666667 1.0000000 2.500000 5
## [14723] {approach,
## network,
## propos} => {neural} 0.1666667 0.8333333 2.500000 5
## [14724] {approach,
## model,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14725] {model,
## network,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [14726] {approach,
## featur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14727] {featur,
## network,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [14728] {dataset,
## neural,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [14729] {represent,
## neural,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [14730] {network,
## represent,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [14731] {show,
## neural,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [14732] {network,
## show,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [14733] {dataset,
## neural,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [14734] {neural,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14735] {dataset,
## neural,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [14736] {featur,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14737] {featur,
## dataset,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [14738] {dataset,
## neural,
## perform} => {network} 0.1000000 0.7500000 1.184211 3
## [14739] {network,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14740] {network,
## dataset,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [14741] {data,
## dataset,
## neural} => {represent} 0.1000000 1.0000000 2.000000 3
## [14742] {data,
## represent,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14743] {represent,
## dataset,
## neural} => {data} 0.1000000 1.0000000 2.307692 3
## [14744] {show,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [14745] {show,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14746] {show,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14747] {model,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [14748] {model,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14749] {dataset,
## neural,
## propos} => {network} 0.1333333 0.8000000 1.263158 4
## [14750] {network,
## dataset,
## neural} => {propos} 0.1333333 0.8000000 1.600000 4
## [14751] {network,
## neural,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14752] {represent,
## neural,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [14753] {neural,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [14754] {represent,
## neural,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [14755] {represent,
## neural,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [14756] {featur,
## neural,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [14757] {featur,
## represent,
## neural} => {learn} 0.1000000 0.7500000 1.730769 3
## [14758] {represent,
## neural,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [14759] {network,
## neural,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [14760] {represent,
## show,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14761] {featur,
## represent,
## neural} => {network} 0.1000000 0.7500000 1.184211 3
## [14762] {featur,
## network,
## neural} => {represent} 0.1000000 0.7500000 1.500000 3
## [14763] {show,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [14764] {network,
## show,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [14765] {model,
## show,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14766] {model,
## network,
## neural} => {show} 0.1000000 0.7500000 1.406250 3
## [14767] {method,
## algorithm,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [14768] {method,
## show,
## algorithm} => {perform} 0.1000000 0.7500000 1.607143 3
## [14769] {show,
## algorithm,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [14770] {method,
## show,
## algorithm} => {model} 0.1000000 0.7500000 1.406250 3
## [14771] {method,
## model,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [14772] {approach,
## method,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [14773] {method,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [14774] {approach,
## method,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [14775] {approach,
## method,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [14776] {featur,
## method,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [14777] {approach,
## method,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [14778] {method,
## network,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [14779] {approach,
## network,
## task} => {method} 0.1000000 0.7500000 2.045455 3
## [14780] {method,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [14781] {featur,
## method,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [14782] {featur,
## method,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [14783] {method,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [14784] {method,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [14785] {method,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [14786] {featur,
## method,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [14787] {method,
## network,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [14788] {approach,
## method,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [14789] {method,
## network,
## train} => {approach} 0.1000000 1.0000000 2.500000 3
## [14790] {approach,
## method,
## perform} => {dataset} 0.1333333 1.0000000 2.307692 4
## [14791] {approach,
## method,
## dataset} => {perform} 0.1333333 0.8000000 1.714286 4
## [14792] {method,
## dataset,
## perform} => {approach} 0.1333333 0.8000000 2.000000 4
## [14793] {approach,
## dataset,
## perform} => {method} 0.1333333 0.8000000 2.181818 4
## [14794] {approach,
## method,
## perform} => {show} 0.1333333 1.0000000 1.875000 4
## [14795] {approach,
## show,
## perform} => {method} 0.1333333 1.0000000 2.727273 4
## [14796] {approach,
## method,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [14797] {approach,
## method,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [14798] {method,
## model,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [14799] {approach,
## model,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [14800] {approach,
## method,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14801] {method,
## dataset,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [14802] {approach,
## method,
## dataset} => {show} 0.1666667 1.0000000 1.875000 5
## [14803] {approach,
## method,
## show} => {dataset} 0.1666667 0.8333333 1.923077 5
## [14804] {method,
## show,
## dataset} => {approach} 0.1666667 1.0000000 2.500000 5
## [14805] {approach,
## show,
## dataset} => {method} 0.1666667 0.8333333 2.272727 5
## [14806] {approach,
## method,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [14807] {approach,
## method,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14808] {method,
## dataset,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [14809] {approach,
## method,
## dataset} => {model} 0.1333333 0.8000000 1.500000 4
## [14810] {approach,
## method,
## model} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14811] {method,
## model,
## dataset} => {approach} 0.1333333 1.0000000 2.500000 4
## [14812] {featur,
## method,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [14813] {approach,
## featur,
## dataset} => {method} 0.1000000 0.7500000 2.045455 3
## [14814] {method,
## network,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [14815] {approach,
## method,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [14816] {approach,
## method,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [14817] {method,
## represent,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [14818] {approach,
## method,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [14819] {method,
## show,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [14820] {approach,
## method,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [14821] {method,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [14822] {approach,
## method,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [14823] {method,
## model,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [14824] {approach,
## method,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [14825] {featur,
## method,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [14826] {approach,
## method,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [14827] {method,
## represent,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [14828] {approach,
## method,
## represent} => {propos} 0.1000000 0.7500000 1.500000 3
## [14829] {method,
## represent,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [14830] {approach,
## method,
## represent} => {model} 0.1000000 0.7500000 1.406250 3
## [14831] {method,
## model,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [14832] {approach,
## method,
## represent} => {featur} 0.1000000 0.7500000 1.406250 3
## [14833] {featur,
## method,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [14834] {approach,
## method,
## represent} => {network} 0.1000000 0.7500000 1.184211 3
## [14835] {method,
## network,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [14836] {approach,
## method,
## propos} => {show} 0.1333333 0.8000000 1.500000 4
## [14837] {method,
## show,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [14838] {approach,
## show,
## propos} => {method} 0.1333333 0.8000000 2.181818 4
## [14839] {approach,
## method,
## show} => {model} 0.1666667 0.8333333 1.562500 5
## [14840] {approach,
## method,
## model} => {show} 0.1666667 1.0000000 1.875000 5
## [14841] {method,
## model,
## show} => {approach} 0.1666667 0.7142857 1.785714 5
## [14842] {approach,
## model,
## show} => {method} 0.1666667 0.8333333 2.272727 5
## [14843] {approach,
## featur,
## method} => {show} 0.1333333 0.8000000 1.500000 4
## [14844] {featur,
## method,
## show} => {approach} 0.1333333 0.8000000 2.000000 4
## [14845] {approach,
## featur,
## show} => {method} 0.1333333 0.8000000 2.181818 4
## [14846] {approach,
## method,
## network} => {show} 0.1333333 0.8000000 1.500000 4
## [14847] {method,
## network,
## show} => {approach} 0.1333333 1.0000000 2.500000 4
## [14848] {method,
## model,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [14849] {approach,
## method,
## propos} => {network} 0.1333333 0.8000000 1.263158 4
## [14850] {approach,
## method,
## network} => {propos} 0.1333333 0.8000000 1.600000 4
## [14851] {method,
## network,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [14852] {approach,
## method,
## model} => {featur} 0.1333333 0.8000000 1.500000 4
## [14853] {approach,
## featur,
## method} => {model} 0.1333333 0.8000000 1.500000 4
## [14854] {featur,
## method,
## model} => {approach} 0.1333333 1.0000000 2.500000 4
## [14855] {method,
## model,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [14856] {approach,
## featur,
## method} => {network} 0.1333333 0.8000000 1.263158 4
## [14857] {approach,
## method,
## network} => {featur} 0.1333333 0.8000000 1.500000 4
## [14858] {featur,
## method,
## network} => {approach} 0.1333333 0.8000000 2.000000 4
## [14859] {approach,
## featur,
## network} => {method} 0.1333333 0.8000000 2.181818 4
## [14860] {method,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [14861] {method,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14862] {method,
## dataset,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [14863] {method,
## network,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14864] {method,
## network,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [14865] {method,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [14866] {method,
## network,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [14867] {method,
## dataset,
## perform} => {show} 0.1333333 0.8000000 1.500000 4
## [14868] {method,
## show,
## dataset} => {perform} 0.1333333 0.8000000 1.714286 4
## [14869] {show,
## dataset,
## perform} => {method} 0.1333333 1.0000000 2.727273 4
## [14870] {method,
## dataset,
## perform} => {propos} 0.1333333 0.8000000 1.600000 4
## [14871] {method,
## perform,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14872] {method,
## dataset,
## propos} => {perform} 0.1333333 0.8000000 1.714286 4
## [14873] {method,
## model,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14874] {method,
## model,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [14875] {featur,
## method,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14876] {featur,
## method,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [14877] {method,
## network,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14878] {method,
## network,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [14879] {network,
## dataset,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [14880] {method,
## perform,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [14881] {method,
## show,
## learn} => {perform} 0.1000000 0.7500000 1.607143 3
## [14882] {show,
## perform,
## learn} => {method} 0.1000000 0.7500000 2.045455 3
## [14883] {method,
## perform,
## propos} => {show} 0.1333333 0.8000000 1.500000 4
## [14884] {method,
## show,
## propos} => {perform} 0.1333333 0.8000000 1.714286 4
## [14885] {method,
## model,
## perform} => {show} 0.1333333 1.0000000 1.875000 4
## [14886] {model,
## show,
## perform} => {method} 0.1333333 0.8000000 2.181818 4
## [14887] {featur,
## method,
## perform} => {show} 0.1000000 0.7500000 1.406250 3
## [14888] {featur,
## show,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [14889] {featur,
## method,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [14890] {method,
## network,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [14891] {network,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [14892] {data,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [14893] {data,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [14894] {method,
## dataset,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [14895] {method,
## show,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14896] {method,
## dataset,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [14897] {method,
## model,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [14898] {method,
## model,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14899] {method,
## show,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [14900] {method,
## dataset,
## propos} => {show} 0.1333333 0.8000000 1.500000 4
## [14901] {method,
## show,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14902] {show,
## dataset,
## propos} => {method} 0.1333333 0.8000000 2.181818 4
## [14903] {method,
## show,
## dataset} => {model} 0.1333333 0.8000000 1.500000 4
## [14904] {method,
## model,
## dataset} => {show} 0.1333333 1.0000000 1.875000 4
## [14905] {model,
## show,
## dataset} => {method} 0.1333333 1.0000000 2.727273 4
## [14906] {featur,
## method,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [14907] {featur,
## show,
## dataset} => {method} 0.1000000 0.7500000 2.045455 3
## [14908] {method,
## network,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [14909] {method,
## network,
## show} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14910] {method,
## model,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [14911] {method,
## model,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14912] {featur,
## method,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [14913] {method,
## dataset,
## propos} => {network} 0.1333333 0.8000000 1.263158 4
## [14914] {method,
## network,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [14915] {method,
## network,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [14916] {method,
## model,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [14917] {featur,
## method,
## dataset} => {model} 0.1000000 0.7500000 1.406250 3
## [14918] {featur,
## method,
## model} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14919] {featur,
## method,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [14920] {method,
## network,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [14921] {method,
## represent,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [14922] {method,
## show,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [14923] {method,
## represent,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [14924] {method,
## represent,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [14925] {method,
## propos,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [14926] {method,
## represent,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [14927] {method,
## represent,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [14928] {featur,
## method,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [14929] {featur,
## method,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [14930] {method,
## show,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [14931] {method,
## propos,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [14932] {method,
## show,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [14933] {method,
## model,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [14934] {method,
## show,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [14935] {featur,
## method,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [14936] {method,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [14937] {featur,
## method,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [14938] {method,
## represent,
## show} => {propos} 0.1000000 0.7500000 1.500000 3
## [14939] {method,
## represent,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [14940] {method,
## represent,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [14941] {method,
## model,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [14942] {method,
## represent,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [14943] {featur,
## method,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [14944] {method,
## represent,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [14945] {featur,
## method,
## represent} => {propos} 0.1000000 0.7500000 1.500000 3
## [14946] {featur,
## method,
## represent} => {network} 0.1000000 0.7500000 1.184211 3
## [14947] {method,
## network,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [14948] {method,
## model,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [14949] {model,
## show,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [14950] {featur,
## show,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [14951] {method,
## network,
## show} => {propos} 0.1000000 0.7500000 1.500000 3
## [14952] {network,
## show,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [14953] {featur,
## method,
## show} => {model} 0.1333333 0.8000000 1.500000 4
## [14954] {featur,
## method,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [14955] {method,
## network,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [14956] {method,
## model,
## network} => {show} 0.1000000 1.0000000 1.875000 3
## [14957] {method,
## network,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [14958] {featur,
## method,
## propos} => {network} 0.1333333 0.8000000 1.263158 4
## [14959] {method,
## network,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [14960] {featur,
## method,
## network} => {propos} 0.1333333 0.8000000 1.600000 4
## [14961] {featur,
## network,
## propos} => {method} 0.1333333 0.8000000 2.181818 4
## [14962] {featur,
## method,
## model} => {network} 0.1000000 0.7500000 1.184211 3
## [14963] {method,
## model,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [14964] {data,
## task,
## algorithm} => {featur} 0.1000000 1.0000000 1.875000 3
## [14965] {featur,
## task,
## algorithm} => {data} 0.1000000 1.0000000 2.307692 3
## [14966] {data,
## featur,
## algorithm} => {task} 0.1000000 0.7500000 2.045455 3
## [14967] {train,
## algorithm,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [14968] {network,
## train,
## algorithm} => {work} 0.1000000 0.7500000 1.875000 3
## [14969] {network,
## algorithm,
## work} => {train} 0.1000000 0.7500000 1.875000 3
## [14970] {network,
## train,
## work} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14971] {train,
## algorithm,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [14972] {network,
## train,
## algorithm} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14973] {network,
## algorithm,
## dataset} => {train} 0.1000000 0.7500000 1.875000 3
## [14974] {approach,
## algorithm,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [14975] {approach,
## algorithm,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [14976] {algorithm,
## perform,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [14977] {approach,
## algorithm,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [14978] {approach,
## algorithm,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14979] {algorithm,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [14980] {approach,
## algorithm,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [14981] {approach,
## network,
## algorithm} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14982] {network,
## algorithm,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [14983] {approach,
## show,
## algorithm} => {network} 0.1000000 1.0000000 1.578947 3
## [14984] {approach,
## network,
## algorithm} => {show} 0.1000000 0.7500000 1.406250 3
## [14985] {network,
## show,
## algorithm} => {approach} 0.1000000 1.0000000 2.500000 3
## [14986] {approach,
## algorithm,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [14987] {approach,
## network,
## algorithm} => {propos} 0.1000000 0.7500000 1.500000 3
## [14988] {network,
## algorithm,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [14989] {approach,
## model,
## algorithm} => {network} 0.1000000 1.0000000 1.578947 3
## [14990] {approach,
## network,
## algorithm} => {model} 0.1000000 0.7500000 1.406250 3
## [14991] {model,
## network,
## algorithm} => {approach} 0.1000000 1.0000000 2.500000 3
## [14992] {data,
## algorithm,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [14993] {featur,
## algorithm,
## perform} => {data} 0.1000000 0.7500000 1.730769 3
## [14994] {data,
## featur,
## algorithm} => {perform} 0.1000000 0.7500000 1.607143 3
## [14995] {algorithm,
## dataset,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [14996] {network,
## algorithm,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14997] {network,
## algorithm,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [14998] {network,
## dataset,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14999] {show,
## algorithm,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [15000] {algorithm,
## perform,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [15001] {show,
## algorithm,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [15002] {show,
## algorithm,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [15003] {model,
## algorithm,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [15004] {algorithm,
## perform,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [15005] {featur,
## algorithm,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [15006] {featur,
## algorithm,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [15007] {data,
## represent,
## algorithm} => {featur} 0.1000000 1.0000000 1.875000 3
## [15008] {data,
## featur,
## algorithm} => {represent} 0.1000000 0.7500000 1.500000 3
## [15009] {featur,
## represent,
## algorithm} => {data} 0.1000000 0.7500000 1.730769 3
## [15010] {data,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [15011] {data,
## model,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [15012] {algorithm,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [15013] {network,
## algorithm,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [15014] {network,
## algorithm,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15015] {show,
## algorithm,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [15016] {model,
## algorithm,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [15017] {show,
## algorithm,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [15018] {featur,
## algorithm,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [15019] {featur,
## show,
## algorithm} => {learn} 0.1000000 0.7500000 1.730769 3
## [15020] {model,
## algorithm,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [15021] {featur,
## algorithm,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [15022] {featur,
## model,
## algorithm} => {learn} 0.1000000 0.7500000 1.730769 3
## [15023] {featur,
## show,
## algorithm} => {model} 0.1333333 1.0000000 1.875000 4
## [15024] {featur,
## model,
## algorithm} => {show} 0.1333333 1.0000000 1.875000 4
## [15025] {approach,
## task,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [15026] {represent,
## task,
## train} => {approach} 0.1000000 0.7500000 1.875000 3
## [15027] {approach,
## represent,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [15028] {approach,
## task,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [15029] {network,
## task,
## train} => {approach} 0.1000000 0.7500000 1.875000 3
## [15030] {approach,
## network,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [15031] {data,
## task,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [15032] {task,
## train,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15033] {data,
## train,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [15034] {data,
## task,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [15035] {represent,
## task,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [15036] {data,
## task,
## train} => {show} 0.1333333 1.0000000 1.875000 4
## [15037] {show,
## task,
## train} => {data} 0.1333333 1.0000000 2.307692 4
## [15038] {data,
## show,
## train} => {task} 0.1333333 1.0000000 2.727273 4
## [15039] {data,
## task,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [15040] {featur,
## task,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [15041] {data,
## task,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [15042] {network,
## task,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [15043] {data,
## network,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [15044] {task,
## train,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15045] {represent,
## task,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [15046] {represent,
## train,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [15047] {task,
## train,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [15048] {show,
## task,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [15049] {show,
## train,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [15050] {task,
## train,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15051] {task,
## train,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [15052] {train,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [15053] {task,
## train,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15054] {featur,
## task,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [15055] {featur,
## train,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [15056] {task,
## train,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [15057] {network,
## task,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [15058] {network,
## train,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [15059] {represent,
## task,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [15060] {show,
## task,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [15061] {represent,
## show,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [15062] {represent,
## task,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [15063] {featur,
## task,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [15064] {represent,
## task,
## train} => {network} 0.1333333 1.0000000 1.578947 4
## [15065] {network,
## task,
## train} => {represent} 0.1333333 1.0000000 2.000000 4
## [15066] {network,
## represent,
## task} => {train} 0.1333333 0.8000000 2.000000 4
## [15067] {network,
## represent,
## train} => {task} 0.1333333 0.8000000 2.181818 4
## [15068] {show,
## task,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [15069] {featur,
## task,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [15070] {featur,
## show,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [15071] {show,
## task,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [15072] {network,
## task,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [15073] {network,
## show,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [15074] {network,
## show,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [15075] {featur,
## task,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [15076] {network,
## task,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [15077] {featur,
## network,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [15078] {approach,
## task,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [15079] {data,
## task,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15080] {approach,
## data,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [15081] {approach,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15082] {approach,
## task,
## dataset} => {work} 0.1000000 1.0000000 2.500000 3
## [15083] {task,
## dataset,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15084] {approach,
## dataset,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [15085] {approach,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15086] {approach,
## task,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [15087] {task,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [15088] {approach,
## work,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [15089] {approach,
## task,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [15090] {represent,
## task,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [15091] {approach,
## represent,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [15092] {approach,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [15093] {task,
## propos,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15094] {approach,
## propos,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [15095] {approach,
## task,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [15096] {approach,
## data,
## dataset} => {task} 0.1000000 1.0000000 2.727273 3
## [15097] {approach,
## task,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15098] {approach,
## data,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [15099] {approach,
## data,
## task} => {represent} 0.1666667 1.0000000 2.000000 5
## [15100] {approach,
## represent,
## task} => {data} 0.1666667 0.8333333 1.923077 5
## [15101] {data,
## represent,
## task} => {approach} 0.1666667 0.7142857 1.785714 5
## [15102] {approach,
## data,
## represent} => {task} 0.1666667 1.0000000 2.727273 5
## [15103] {approach,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [15104] {approach,
## data,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [15105] {approach,
## data,
## task} => {propos} 0.1333333 0.8000000 1.600000 4
## [15106] {approach,
## task,
## propos} => {data} 0.1333333 0.8000000 1.846154 4
## [15107] {approach,
## data,
## propos} => {task} 0.1333333 1.0000000 2.727273 4
## [15108] {approach,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [15109] {approach,
## data,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [15110] {approach,
## data,
## task} => {featur} 0.1333333 0.8000000 1.500000 4
## [15111] {approach,
## featur,
## task} => {data} 0.1333333 0.8000000 1.846154 4
## [15112] {approach,
## data,
## featur} => {task} 0.1333333 1.0000000 2.727273 4
## [15113] {approach,
## network,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [15114] {approach,
## data,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [15115] {approach,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [15116] {approach,
## task,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15117] {approach,
## task,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [15118] {represent,
## task,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [15119] {approach,
## represent,
## dataset} => {task} 0.1000000 0.7500000 2.045455 3
## [15120] {approach,
## task,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [15121] {task,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [15122] {approach,
## task,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [15123] {approach,
## task,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [15124] {approach,
## task,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [15125] {approach,
## propos,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [15126] {approach,
## task,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15127] {approach,
## task,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [15128] {approach,
## network,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [15129] {approach,
## network,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [15130] {approach,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [15131] {approach,
## represent,
## task} => {propos} 0.1666667 0.8333333 1.666667 5
## [15132] {approach,
## task,
## propos} => {represent} 0.1666667 1.0000000 2.000000 5
## [15133] {represent,
## task,
## propos} => {approach} 0.1666667 1.0000000 2.500000 5
## [15134] {approach,
## represent,
## propos} => {task} 0.1666667 0.8333333 2.272727 5
## [15135] {approach,
## model,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [15136] {model,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [15137] {approach,
## represent,
## task} => {featur} 0.1666667 0.8333333 1.562500 5
## [15138] {approach,
## featur,
## task} => {represent} 0.1666667 1.0000000 2.000000 5
## [15139] {featur,
## represent,
## task} => {approach} 0.1666667 0.7142857 1.785714 5
## [15140] {approach,
## featur,
## represent} => {task} 0.1666667 0.8333333 2.272727 5
## [15141] {approach,
## network,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [15142] {network,
## represent,
## task} => {approach} 0.1333333 0.8000000 2.000000 4
## [15143] {approach,
## network,
## represent} => {task} 0.1333333 0.8000000 2.181818 4
## [15144] {approach,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [15145] {approach,
## network,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [15146] {network,
## show,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [15147] {approach,
## task,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [15148] {approach,
## featur,
## task} => {propos} 0.1333333 0.8000000 1.600000 4
## [15149] {approach,
## featur,
## propos} => {task} 0.1333333 0.8000000 2.181818 4
## [15150] {approach,
## network,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [15151] {network,
## task,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [15152] {approach,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [15153] {approach,
## network,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [15154] {data,
## task,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15155] {task,
## dataset,
## work} => {data} 0.1333333 1.0000000 2.307692 4
## [15156] {data,
## task,
## dataset} => {work} 0.1333333 0.8000000 2.000000 4
## [15157] {data,
## dataset,
## work} => {task} 0.1333333 0.8000000 2.181818 4
## [15158] {data,
## task,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [15159] {task,
## work,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [15160] {data,
## work,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [15161] {data,
## task,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15162] {represent,
## task,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [15163] {data,
## represent,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [15164] {data,
## task,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [15165] {task,
## propos,
## work} => {data} 0.1333333 1.0000000 2.307692 4
## [15166] {data,
## propos,
## work} => {task} 0.1333333 1.0000000 2.727273 4
## [15167] {data,
## task,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [15168] {model,
## task,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [15169] {data,
## model,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [15170] {data,
## task,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [15171] {featur,
## task,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [15172] {data,
## featur,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [15173] {data,
## task,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15174] {network,
## task,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [15175] {data,
## network,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [15176] {task,
## dataset,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [15177] {task,
## work,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15178] {task,
## dataset,
## learn} => {work} 0.1333333 0.8000000 2.000000 4
## [15179] {dataset,
## work,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [15180] {task,
## dataset,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15181] {represent,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15182] {represent,
## task,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [15183] {represent,
## dataset,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [15184] {task,
## dataset,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [15185] {task,
## propos,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15186] {task,
## dataset,
## propos} => {work} 0.1333333 1.0000000 2.500000 4
## [15187] {task,
## dataset,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [15188] {model,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15189] {model,
## task,
## dataset} => {work} 0.1000000 1.0000000 2.500000 3
## [15190] {model,
## dataset,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [15191] {task,
## dataset,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [15192] {featur,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15193] {featur,
## task,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [15194] {task,
## dataset,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15195] {network,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15196] {network,
## task,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [15197] {task,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15198] {represent,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15199] {represent,
## work,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [15200] {task,
## work,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [15201] {task,
## propos,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [15202] {propos,
## work,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [15203] {task,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [15204] {model,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15205] {model,
## work,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [15206] {task,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15207] {featur,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15208] {featur,
## work,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [15209] {task,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [15210] {network,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15211] {network,
## work,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [15212] {represent,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [15213] {task,
## propos,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15214] {represent,
## propos,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [15215] {task,
## propos,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [15216] {model,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [15217] {model,
## task,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [15218] {model,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [15219] {task,
## propos,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [15220] {featur,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [15221] {featur,
## propos,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [15222] {task,
## propos,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15223] {network,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [15224] {network,
## task,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [15225] {model,
## task,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [15226] {featur,
## task,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [15227] {featur,
## model,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [15228] {data,
## task,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [15229] {task,
## perform,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [15230] {data,
## perform,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [15231] {data,
## task,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [15232] {featur,
## task,
## perform} => {data} 0.1000000 1.0000000 2.307692 3
## [15233] {task,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [15234] {featur,
## task,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [15235] {data,
## task,
## dataset} => {learn} 0.1666667 1.0000000 2.307692 5
## [15236] {data,
## task,
## learn} => {dataset} 0.1666667 0.7142857 1.648352 5
## [15237] {task,
## dataset,
## learn} => {data} 0.1666667 1.0000000 2.307692 5
## [15238] {data,
## dataset,
## learn} => {task} 0.1666667 0.8333333 2.272727 5
## [15239] {data,
## task,
## dataset} => {represent} 0.1333333 0.8000000 1.600000 4
## [15240] {represent,
## task,
## dataset} => {data} 0.1333333 1.0000000 2.307692 4
## [15241] {show,
## task,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [15242] {data,
## show,
## dataset} => {task} 0.1000000 1.0000000 2.727273 3
## [15243] {data,
## task,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [15244] {task,
## dataset,
## propos} => {data} 0.1333333 1.0000000 2.307692 4
## [15245] {data,
## dataset,
## propos} => {task} 0.1333333 0.8000000 2.181818 4
## [15246] {model,
## task,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [15247] {data,
## model,
## dataset} => {task} 0.1000000 0.7500000 2.045455 3
## [15248] {data,
## task,
## dataset} => {featur} 0.1333333 0.8000000 1.500000 4
## [15249] {featur,
## task,
## dataset} => {data} 0.1333333 1.0000000 2.307692 4
## [15250] {data,
## task,
## dataset} => {network} 0.1333333 0.8000000 1.263158 4
## [15251] {network,
## task,
## dataset} => {data} 0.1333333 1.0000000 2.307692 4
## [15252] {data,
## network,
## dataset} => {task} 0.1333333 0.8000000 2.181818 4
## [15253] {data,
## task,
## learn} => {represent} 0.1666667 0.7142857 1.428571 5
## [15254] {data,
## represent,
## task} => {learn} 0.1666667 0.7142857 1.648352 5
## [15255] {represent,
## task,
## learn} => {data} 0.1666667 0.8333333 1.923077 5
## [15256] {data,
## represent,
## learn} => {task} 0.1666667 0.8333333 2.272727 5
## [15257] {data,
## task,
## learn} => {show} 0.1666667 0.7142857 1.339286 5
## [15258] {data,
## show,
## task} => {learn} 0.1666667 0.8333333 1.923077 5
## [15259] {show,
## task,
## learn} => {data} 0.1666667 1.0000000 2.307692 5
## [15260] {data,
## show,
## learn} => {task} 0.1666667 1.0000000 2.727273 5
## [15261] {data,
## task,
## learn} => {propos} 0.1666667 0.7142857 1.428571 5
## [15262] {data,
## task,
## propos} => {learn} 0.1666667 0.8333333 1.923077 5
## [15263] {task,
## propos,
## learn} => {data} 0.1666667 0.8333333 1.923077 5
## [15264] {data,
## propos,
## learn} => {task} 0.1666667 0.8333333 2.272727 5
## [15265] {data,
## task,
## learn} => {model} 0.1666667 0.7142857 1.339286 5
## [15266] {data,
## model,
## task} => {learn} 0.1666667 0.8333333 1.923077 5
## [15267] {model,
## task,
## learn} => {data} 0.1666667 1.0000000 2.307692 5
## [15268] {data,
## model,
## learn} => {task} 0.1666667 0.8333333 2.272727 5
## [15269] {data,
## task,
## learn} => {featur} 0.2000000 0.8571429 1.607143 6
## [15270] {data,
## featur,
## task} => {learn} 0.2000000 0.7500000 1.730769 6
## [15271] {featur,
## task,
## learn} => {data} 0.2000000 0.8571429 1.978022 6
## [15272] {data,
## featur,
## learn} => {task} 0.2000000 0.8571429 2.337662 6
## [15273] {network,
## task,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [15274] {data,
## network,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [15275] {data,
## represent,
## task} => {show} 0.1666667 0.7142857 1.339286 5
## [15276] {data,
## show,
## task} => {represent} 0.1666667 0.8333333 1.666667 5
## [15277] {represent,
## show,
## task} => {data} 0.1666667 1.0000000 2.307692 5
## [15278] {data,
## represent,
## show} => {task} 0.1666667 1.0000000 2.727273 5
## [15279] {represent,
## task,
## propos} => {data} 0.1333333 0.8000000 1.846154 4
## [15280] {data,
## represent,
## propos} => {task} 0.1333333 0.8000000 2.181818 4
## [15281] {model,
## represent,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [15282] {data,
## model,
## represent} => {task} 0.1333333 0.8000000 2.181818 4
## [15283] {data,
## represent,
## task} => {featur} 0.2000000 0.8571429 1.607143 6
## [15284] {data,
## featur,
## task} => {represent} 0.2000000 0.7500000 1.500000 6
## [15285] {featur,
## represent,
## task} => {data} 0.2000000 0.8571429 1.978022 6
## [15286] {data,
## featur,
## represent} => {task} 0.2000000 0.7500000 2.045455 6
## [15287] {network,
## represent,
## task} => {data} 0.1333333 0.8000000 1.846154 4
## [15288] {data,
## network,
## represent} => {task} 0.1333333 0.8000000 2.181818 4
## [15289] {show,
## task,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [15290] {data,
## show,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [15291] {model,
## show,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [15292] {data,
## model,
## show} => {task} 0.1333333 0.8000000 2.181818 4
## [15293] {data,
## show,
## task} => {featur} 0.1666667 0.8333333 1.562500 5
## [15294] {featur,
## show,
## task} => {data} 0.1666667 1.0000000 2.307692 5
## [15295] {data,
## featur,
## show} => {task} 0.1666667 1.0000000 2.727273 5
## [15296] {network,
## show,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [15297] {data,
## network,
## show} => {task} 0.1333333 1.0000000 2.727273 4
## [15298] {model,
## task,
## propos} => {data} 0.1333333 1.0000000 2.307692 4
## [15299] {data,
## model,
## propos} => {task} 0.1333333 0.8000000 2.181818 4
## [15300] {data,
## task,
## propos} => {featur} 0.1666667 0.8333333 1.562500 5
## [15301] {featur,
## task,
## propos} => {data} 0.1666667 0.8333333 1.923077 5
## [15302] {data,
## featur,
## propos} => {task} 0.1666667 0.8333333 2.272727 5
## [15303] {network,
## task,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [15304] {data,
## network,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [15305] {data,
## model,
## task} => {featur} 0.2000000 1.0000000 1.875000 6
## [15306] {data,
## featur,
## task} => {model} 0.2000000 0.7500000 1.406250 6
## [15307] {featur,
## model,
## task} => {data} 0.2000000 1.0000000 2.307692 6
## [15308] {data,
## featur,
## model} => {task} 0.2000000 0.8571429 2.337662 6
## [15309] {model,
## network,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [15310] {data,
## model,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [15311] {featur,
## network,
## task} => {data} 0.1333333 0.8000000 1.846154 4
## [15312] {data,
## featur,
## network} => {task} 0.1333333 0.8000000 2.181818 4
## [15313] {task,
## dataset,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [15314] {represent,
## task,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [15315] {show,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [15316] {task,
## dataset,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [15317] {task,
## dataset,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [15318] {model,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [15319] {task,
## dataset,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [15320] {featur,
## task,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [15321] {task,
## dataset,
## learn} => {network} 0.1333333 0.8000000 1.263158 4
## [15322] {network,
## task,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [15323] {network,
## task,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15324] {network,
## dataset,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [15325] {represent,
## task,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [15326] {show,
## task,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [15327] {represent,
## show,
## dataset} => {task} 0.1000000 0.7500000 2.045455 3
## [15328] {represent,
## task,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [15329] {task,
## dataset,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [15330] {represent,
## task,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [15331] {featur,
## task,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [15332] {represent,
## task,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [15333] {network,
## task,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [15334] {network,
## represent,
## dataset} => {task} 0.1000000 0.7500000 2.045455 3
## [15335] {show,
## task,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [15336] {network,
## task,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [15337] {network,
## show,
## task} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15338] {task,
## dataset,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [15339] {model,
## task,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [15340] {model,
## task,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15341] {task,
## dataset,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [15342] {featur,
## task,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [15343] {task,
## dataset,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [15344] {network,
## task,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [15345] {network,
## task,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15346] {model,
## task,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [15347] {featur,
## task,
## dataset} => {model} 0.1000000 0.7500000 1.406250 3
## [15348] {featur,
## task,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [15349] {network,
## task,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [15350] {show,
## task,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [15351] {represent,
## show,
## task} => {learn} 0.1333333 0.8000000 1.846154 4
## [15352] {represent,
## task,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [15353] {model,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [15354] {represent,
## task,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [15355] {featur,
## task,
## learn} => {represent} 0.1666667 0.7142857 1.428571 5
## [15356] {featur,
## represent,
## task} => {learn} 0.1666667 0.7142857 1.648352 5
## [15357] {network,
## task,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [15358] {network,
## represent,
## task} => {learn} 0.1333333 0.8000000 1.846154 4
## [15359] {network,
## represent,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [15360] {show,
## task,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [15361] {model,
## show,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [15362] {show,
## task,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [15363] {featur,
## show,
## task} => {learn} 0.1333333 0.8000000 1.846154 4
## [15364] {network,
## show,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [15365] {network,
## show,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [15366] {model,
## task,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [15367] {model,
## task,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [15368] {task,
## propos,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [15369] {featur,
## task,
## learn} => {propos} 0.1666667 0.7142857 1.428571 5
## [15370] {featur,
## task,
## propos} => {learn} 0.1666667 0.8333333 1.923077 5
## [15371] {featur,
## propos,
## learn} => {task} 0.1666667 0.7142857 1.948052 5
## [15372] {network,
## task,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [15373] {network,
## task,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [15374] {network,
## propos,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [15375] {model,
## task,
## learn} => {featur} 0.1666667 1.0000000 1.875000 5
## [15376] {featur,
## task,
## learn} => {model} 0.1666667 0.7142857 1.339286 5
## [15377] {featur,
## model,
## task} => {learn} 0.1666667 0.8333333 1.923077 5
## [15378] {network,
## task,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [15379] {featur,
## network,
## task} => {learn} 0.1333333 0.8000000 1.846154 4
## [15380] {featur,
## network,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [15381] {model,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [15382] {model,
## show,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [15383] {represent,
## show,
## task} => {featur} 0.1333333 0.8000000 1.500000 4
## [15384] {featur,
## show,
## task} => {represent} 0.1333333 0.8000000 1.600000 4
## [15385] {represent,
## show,
## task} => {network} 0.1333333 0.8000000 1.263158 4
## [15386] {network,
## represent,
## task} => {show} 0.1333333 0.8000000 1.500000 4
## [15387] {network,
## show,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [15388] {represent,
## task,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [15389] {network,
## task,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [15390] {network,
## represent,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [15391] {model,
## represent,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [15392] {network,
## represent,
## task} => {featur} 0.1333333 0.8000000 1.500000 4
## [15393] {featur,
## network,
## task} => {represent} 0.1333333 0.8000000 1.600000 4
## [15394] {model,
## show,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [15395] {featur,
## show,
## task} => {model} 0.1333333 0.8000000 1.500000 4
## [15396] {network,
## show,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [15397] {model,
## task,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [15398] {network,
## task,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [15399] {model,
## network,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [15400] {approach,
## train,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [15401] {approach,
## train,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15402] {train,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [15403] {approach,
## train,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [15404] {approach,
## represent,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [15405] {approach,
## show,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [15406] {network,
## show,
## train} => {approach} 0.1000000 0.7500000 1.875000 3
## [15407] {approach,
## train,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [15408] {approach,
## network,
## train} => {propos} 0.1333333 0.8000000 1.600000 4
## [15409] {network,
## train,
## propos} => {approach} 0.1333333 1.0000000 2.500000 4
## [15410] {train,
## dataset,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [15411] {network,
## train,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15412] {show,
## train,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [15413] {train,
## propos,
## work} => {show} 0.1000000 1.0000000 1.875000 3
## [15414] {show,
## train,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [15415] {show,
## propos,
## work} => {train} 0.1000000 0.7500000 1.875000 3
## [15416] {data,
## train,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [15417] {featur,
## train,
## perform} => {data} 0.1000000 1.0000000 2.307692 3
## [15418] {show,
## train,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [15419] {train,
## perform,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [15420] {show,
## train,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [15421] {data,
## train,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [15422] {data,
## train,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15423] {train,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [15424] {data,
## train,
## dataset} => {represent} 0.1333333 1.0000000 2.000000 4
## [15425] {data,
## represent,
## train} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15426] {represent,
## train,
## dataset} => {data} 0.1333333 1.0000000 2.307692 4
## [15427] {data,
## train,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [15428] {featur,
## train,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [15429] {data,
## train,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [15430] {data,
## network,
## train} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15431] {data,
## train,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15432] {represent,
## train,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15433] {data,
## train,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [15434] {data,
## show,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [15435] {show,
## train,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [15436] {data,
## train,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15437] {data,
## train,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [15438] {train,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15439] {data,
## train,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15440] {featur,
## train,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15441] {data,
## show,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [15442] {represent,
## show,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [15443] {data,
## represent,
## train} => {featur} 0.1333333 0.8000000 1.500000 4
## [15444] {data,
## featur,
## train} => {represent} 0.1333333 0.8000000 1.600000 4
## [15445] {featur,
## represent,
## train} => {data} 0.1333333 0.8000000 1.846154 4
## [15446] {data,
## represent,
## train} => {network} 0.1333333 0.8000000 1.263158 4
## [15447] {data,
## network,
## train} => {represent} 0.1333333 1.0000000 2.000000 4
## [15448] {network,
## represent,
## train} => {data} 0.1333333 0.8000000 1.846154 4
## [15449] {data,
## network,
## represent} => {train} 0.1333333 0.8000000 2.000000 4
## [15450] {data,
## show,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [15451] {featur,
## show,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [15452] {data,
## show,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [15453] {data,
## network,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [15454] {network,
## show,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [15455] {data,
## network,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [15456] {data,
## model,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [15457] {featur,
## model,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [15458] {data,
## network,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [15459] {featur,
## network,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [15460] {train,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [15461] {represent,
## train,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [15462] {represent,
## train,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15463] {represent,
## train,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [15464] {featur,
## train,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [15465] {represent,
## train,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [15466] {network,
## represent,
## dataset} => {train} 0.1000000 0.7500000 1.875000 3
## [15467] {show,
## train,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [15468] {network,
## show,
## train} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15469] {train,
## dataset,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [15470] {network,
## train,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15471] {represent,
## train,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15472] {train,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15473] {represent,
## train,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [15474] {represent,
## train,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15475] {featur,
## train,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15476] {represent,
## train,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [15477] {network,
## train,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [15478] {train,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15479] {featur,
## train,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15480] {featur,
## train,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [15481] {represent,
## show,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [15482] {network,
## show,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [15483] {featur,
## represent,
## train} => {network} 0.1333333 0.8000000 1.263158 4
## [15484] {network,
## represent,
## train} => {featur} 0.1333333 0.8000000 1.500000 4
## [15485] {featur,
## network,
## train} => {represent} 0.1333333 1.0000000 2.000000 4
## [15486] {approach,
## data,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15487] {approach,
## dataset,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [15488] {approach,
## data,
## dataset} => {work} 0.1000000 1.0000000 2.500000 3
## [15489] {approach,
## data,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15490] {approach,
## work,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15491] {approach,
## data,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [15492] {data,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [15493] {approach,
## data,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [15494] {approach,
## represent,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [15495] {data,
## represent,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15496] {approach,
## data,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [15497] {approach,
## propos,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [15498] {approach,
## data,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [15499] {data,
## propos,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15500] {approach,
## dataset,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15501] {approach,
## work,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15502] {dataset,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [15503] {approach,
## dataset,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15504] {approach,
## represent,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15505] {approach,
## represent,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [15506] {represent,
## dataset,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15507] {approach,
## dataset,
## work} => {show} 0.1000000 0.7500000 1.406250 3
## [15508] {approach,
## show,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15509] {show,
## dataset,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [15510] {approach,
## dataset,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [15511] {approach,
## propos,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15512] {approach,
## dataset,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15513] {approach,
## network,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15514] {approach,
## work,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [15515] {approach,
## represent,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [15516] {represent,
## work,
## learn} => {approach} 0.1333333 1.0000000 2.500000 4
## [15517] {approach,
## work,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [15518] {approach,
## show,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15519] {show,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [15520] {approach,
## work,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15521] {approach,
## propos,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15522] {propos,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [15523] {approach,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [15524] {approach,
## model,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15525] {model,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [15526] {approach,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15527] {approach,
## featur,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15528] {featur,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [15529] {approach,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [15530] {approach,
## network,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15531] {approach,
## network,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [15532] {network,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [15533] {approach,
## represent,
## work} => {show} 0.1000000 0.7500000 1.406250 3
## [15534] {approach,
## show,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15535] {represent,
## show,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15536] {approach,
## represent,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [15537] {approach,
## propos,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15538] {represent,
## propos,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15539] {approach,
## represent,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [15540] {approach,
## model,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [15541] {model,
## represent,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [15542] {approach,
## represent,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [15543] {approach,
## featur,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [15544] {featur,
## represent,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15545] {approach,
## represent,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15546] {approach,
## network,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15547] {network,
## represent,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15548] {approach,
## show,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [15549] {approach,
## propos,
## work} => {show} 0.1000000 0.7500000 1.406250 3
## [15550] {show,
## propos,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15551] {approach,
## show,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [15552] {approach,
## network,
## work} => {show} 0.1333333 1.0000000 1.875000 4
## [15553] {network,
## show,
## work} => {approach} 0.1333333 1.0000000 2.500000 4
## [15554] {approach,
## propos,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15555] {approach,
## network,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [15556] {approach,
## model,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [15557] {approach,
## featur,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [15558] {featur,
## model,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [15559] {approach,
## perform,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15560] {dataset,
## perform,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [15561] {approach,
## dataset,
## perform} => {show} 0.1333333 0.8000000 1.500000 4
## [15562] {approach,
## show,
## perform} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15563] {show,
## dataset,
## perform} => {approach} 0.1333333 1.0000000 2.500000 4
## [15564] {approach,
## dataset,
## perform} => {propos} 0.1333333 0.8000000 1.600000 4
## [15565] {approach,
## perform,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15566] {approach,
## dataset,
## perform} => {model} 0.1333333 0.8000000 1.500000 4
## [15567] {approach,
## model,
## perform} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15568] {model,
## dataset,
## perform} => {approach} 0.1333333 0.8000000 2.000000 4
## [15569] {approach,
## featur,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15570] {approach,
## featur,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [15571] {approach,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [15572] {approach,
## model,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [15573] {approach,
## represent,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [15574] {approach,
## show,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [15575] {approach,
## show,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [15576] {approach,
## model,
## perform} => {show} 0.1000000 0.7500000 1.406250 3
## [15577] {approach,
## model,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [15578] {approach,
## featur,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [15579] {approach,
## model,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [15580] {approach,
## featur,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [15581] {approach,
## data,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [15582] {approach,
## data,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15583] {approach,
## data,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [15584] {approach,
## represent,
## dataset} => {data} 0.1000000 0.7500000 1.730769 3
## [15585] {approach,
## data,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [15586] {approach,
## data,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15587] {approach,
## data,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [15588] {approach,
## data,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [15589] {approach,
## data,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [15590] {approach,
## data,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [15591] {approach,
## data,
## represent} => {propos} 0.1333333 0.8000000 1.600000 4
## [15592] {approach,
## data,
## propos} => {represent} 0.1333333 1.0000000 2.000000 4
## [15593] {data,
## represent,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [15594] {approach,
## data,
## model} => {represent} 0.1000000 1.0000000 2.000000 3
## [15595] {approach,
## data,
## represent} => {featur} 0.1333333 0.8000000 1.500000 4
## [15596] {approach,
## data,
## featur} => {represent} 0.1333333 1.0000000 2.000000 4
## [15597] {approach,
## data,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [15598] {approach,
## data,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [15599] {approach,
## data,
## network} => {show} 0.1000000 1.0000000 1.875000 3
## [15600] {data,
## network,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [15601] {approach,
## data,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [15602] {approach,
## data,
## featur} => {propos} 0.1000000 0.7500000 1.500000 3
## [15603] {approach,
## data,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [15604] {approach,
## data,
## featur} => {model} 0.1000000 0.7500000 1.406250 3
## [15605] {approach,
## dataset,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [15606] {approach,
## represent,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [15607] {approach,
## dataset,
## learn} => {show} 0.1333333 0.8000000 1.500000 4
## [15608] {approach,
## show,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15609] {show,
## dataset,
## learn} => {approach} 0.1333333 0.8000000 2.000000 4
## [15610] {approach,
## dataset,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [15611] {approach,
## propos,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15612] {approach,
## dataset,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [15613] {approach,
## model,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15614] {approach,
## featur,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [15615] {approach,
## represent,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [15616] {represent,
## show,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [15617] {approach,
## represent,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [15618] {represent,
## dataset,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [15619] {approach,
## represent,
## dataset} => {model} 0.1000000 0.7500000 1.406250 3
## [15620] {model,
## represent,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [15621] {approach,
## show,
## dataset} => {propos} 0.1666667 0.8333333 1.666667 5
## [15622] {approach,
## dataset,
## propos} => {show} 0.1666667 0.7142857 1.339286 5
## [15623] {approach,
## show,
## propos} => {dataset} 0.1666667 1.0000000 2.307692 5
## [15624] {show,
## dataset,
## propos} => {approach} 0.1666667 1.0000000 2.500000 5
## [15625] {model,
## show,
## dataset} => {approach} 0.1333333 1.0000000 2.500000 4
## [15626] {approach,
## featur,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [15627] {featur,
## show,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [15628] {approach,
## network,
## dataset} => {show} 0.1333333 0.8000000 1.500000 4
## [15629] {network,
## show,
## dataset} => {approach} 0.1333333 0.8000000 2.000000 4
## [15630] {approach,
## dataset,
## propos} => {model} 0.1666667 0.7142857 1.339286 5
## [15631] {approach,
## model,
## dataset} => {propos} 0.1666667 0.8333333 1.666667 5
## [15632] {approach,
## model,
## propos} => {dataset} 0.1666667 1.0000000 2.307692 5
## [15633] {model,
## dataset,
## propos} => {approach} 0.1666667 0.7142857 1.785714 5
## [15634] {approach,
## featur,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [15635] {approach,
## dataset,
## propos} => {network} 0.1666667 0.7142857 1.127820 5
## [15636] {approach,
## network,
## dataset} => {propos} 0.1666667 1.0000000 2.000000 5
## [15637] {approach,
## network,
## propos} => {dataset} 0.1666667 0.8333333 1.923077 5
## [15638] {network,
## dataset,
## propos} => {approach} 0.1666667 0.7142857 1.785714 5
## [15639] {approach,
## featur,
## dataset} => {model} 0.1333333 1.0000000 1.875000 4
## [15640] {model,
## network,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [15641] {approach,
## show,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [15642] {approach,
## represent,
## show} => {learn} 0.1333333 0.8000000 1.846154 4
## [15643] {approach,
## represent,
## learn} => {propos} 0.1666667 0.8333333 1.666667 5
## [15644] {approach,
## propos,
## learn} => {represent} 0.1666667 1.0000000 2.000000 5
## [15645] {approach,
## represent,
## propos} => {learn} 0.1666667 0.8333333 1.923077 5
## [15646] {represent,
## propos,
## learn} => {approach} 0.1666667 0.7142857 1.785714 5
## [15647] {approach,
## model,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [15648] {approach,
## model,
## represent} => {learn} 0.1333333 0.8000000 1.846154 4
## [15649] {approach,
## featur,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [15650] {approach,
## network,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [15651] {approach,
## network,
## represent} => {learn} 0.1333333 0.8000000 1.846154 4
## [15652] {network,
## represent,
## learn} => {approach} 0.1333333 0.8000000 2.000000 4
## [15653] {approach,
## show,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [15654] {approach,
## model,
## learn} => {show} 0.1333333 0.8000000 1.500000 4
## [15655] {approach,
## network,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [15656] {network,
## show,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [15657] {approach,
## network,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15658] {network,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [15659] {approach,
## model,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [15660] {approach,
## featur,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [15661] {approach,
## network,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15662] {approach,
## represent,
## show} => {model} 0.1333333 0.8000000 1.500000 4
## [15663] {approach,
## model,
## represent} => {show} 0.1333333 0.8000000 1.500000 4
## [15664] {approach,
## represent,
## show} => {network} 0.1333333 0.8000000 1.263158 4
## [15665] {approach,
## network,
## represent} => {show} 0.1333333 0.8000000 1.500000 4
## [15666] {model,
## represent,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [15667] {approach,
## featur,
## propos} => {represent} 0.1333333 0.8000000 1.600000 4
## [15668] {network,
## represent,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [15669] {approach,
## model,
## represent} => {featur} 0.1333333 0.8000000 1.500000 4
## [15670] {model,
## network,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [15671] {approach,
## network,
## represent} => {featur} 0.1333333 0.8000000 1.500000 4
## [15672] {approach,
## featur,
## network} => {represent} 0.1333333 0.8000000 1.600000 4
## [15673] {model,
## show,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [15674] {approach,
## show,
## propos} => {network} 0.1333333 0.8000000 1.263158 4
## [15675] {network,
## show,
## propos} => {approach} 0.1333333 1.0000000 2.500000 4
## [15676] {approach,
## model,
## show} => {featur} 0.1666667 0.8333333 1.562500 5
## [15677] {approach,
## featur,
## show} => {model} 0.1666667 1.0000000 1.875000 5
## [15678] {approach,
## featur,
## model} => {show} 0.1666667 0.8333333 1.562500 5
## [15679] {featur,
## model,
## show} => {approach} 0.1666667 0.7142857 1.785714 5
## [15680] {approach,
## model,
## network} => {show} 0.1333333 0.8000000 1.500000 4
## [15681] {model,
## network,
## show} => {approach} 0.1333333 0.8000000 2.000000 4
## [15682] {approach,
## featur,
## show} => {network} 0.1333333 0.8000000 1.263158 4
## [15683] {approach,
## featur,
## network} => {show} 0.1333333 0.8000000 1.500000 4
## [15684] {featur,
## network,
## show} => {approach} 0.1333333 0.8000000 2.000000 4
## [15685] {model,
## network,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [15686] {approach,
## model,
## network} => {featur} 0.1333333 0.8000000 1.500000 4
## [15687] {approach,
## featur,
## network} => {model} 0.1333333 0.8000000 1.500000 4
## [15688] {featur,
## model,
## network} => {approach} 0.1333333 0.8000000 2.000000 4
## [15689] {dataset,
## perform,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [15690] {perform,
## propos,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15691] {dataset,
## perform,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [15692] {featur,
## perform,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15693] {dataset,
## perform,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15694] {network,
## perform,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15695] {network,
## dataset,
## perform} => {work} 0.1000000 0.7500000 1.875000 3
## [15696] {data,
## dataset,
## work} => {learn} 0.1333333 0.8000000 1.846154 4
## [15697] {data,
## work,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15698] {dataset,
## work,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [15699] {data,
## dataset,
## work} => {represent} 0.1333333 0.8000000 1.600000 4
## [15700] {data,
## represent,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15701] {represent,
## dataset,
## work} => {data} 0.1333333 1.0000000 2.307692 4
## [15702] {data,
## dataset,
## work} => {propos} 0.1333333 0.8000000 1.600000 4
## [15703] {data,
## propos,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15704] {data,
## dataset,
## propos} => {work} 0.1333333 0.8000000 2.000000 4
## [15705] {data,
## model,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15706] {model,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [15707] {data,
## model,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [15708] {data,
## dataset,
## work} => {featur} 0.1333333 0.8000000 1.500000 4
## [15709] {data,
## featur,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15710] {featur,
## dataset,
## work} => {data} 0.1333333 0.8000000 1.846154 4
## [15711] {data,
## dataset,
## work} => {network} 0.1333333 0.8000000 1.263158 4
## [15712] {data,
## network,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15713] {data,
## network,
## dataset} => {work} 0.1333333 0.8000000 2.000000 4
## [15714] {data,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15715] {data,
## represent,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15716] {represent,
## work,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15717] {data,
## work,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [15718] {data,
## propos,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [15719] {propos,
## work,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [15720] {data,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [15721] {data,
## model,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15722] {model,
## work,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15723] {data,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15724] {data,
## featur,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15725] {featur,
## work,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15726] {data,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [15727] {data,
## network,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15728] {network,
## work,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15729] {data,
## network,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [15730] {data,
## represent,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [15731] {data,
## propos,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15732] {represent,
## propos,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [15733] {data,
## represent,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [15734] {data,
## featur,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15735] {featur,
## represent,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [15736] {data,
## represent,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15737] {data,
## network,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15738] {network,
## represent,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [15739] {data,
## propos,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [15740] {data,
## model,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [15741] {model,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [15742] {data,
## propos,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [15743] {data,
## featur,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [15744] {featur,
## propos,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [15745] {data,
## propos,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15746] {data,
## network,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [15747] {data,
## network,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [15748] {data,
## model,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [15749] {data,
## featur,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [15750] {featur,
## model,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [15751] {data,
## featur,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15752] {data,
## network,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [15753] {dataset,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15754] {represent,
## dataset,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15755] {represent,
## work,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15756] {dataset,
## work,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [15757] {propos,
## work,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15758] {dataset,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [15759] {model,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15760] {model,
## work,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15761] {dataset,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15762] {featur,
## work,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15763] {dataset,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [15764] {network,
## work,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15765] {network,
## dataset,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [15766] {represent,
## dataset,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [15767] {represent,
## propos,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15768] {represent,
## dataset,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [15769] {featur,
## represent,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15770] {represent,
## dataset,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15771] {network,
## represent,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15772] {network,
## represent,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [15773] {show,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [15774] {show,
## propos,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15775] {show,
## dataset,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [15776] {network,
## show,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15777] {model,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [15778] {model,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15779] {featur,
## dataset,
## work} => {propos} 0.1333333 0.8000000 1.600000 4
## [15780] {featur,
## propos,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15781] {dataset,
## propos,
## work} => {network} 0.1666667 0.8333333 1.315789 5
## [15782] {network,
## dataset,
## work} => {propos} 0.1666667 0.8333333 1.666667 5
## [15783] {network,
## propos,
## work} => {dataset} 0.1666667 1.0000000 2.307692 5
## [15784] {network,
## dataset,
## propos} => {work} 0.1666667 0.7142857 1.785714 5
## [15785] {model,
## dataset,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [15786] {featur,
## model,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15787] {featur,
## dataset,
## work} => {network} 0.1333333 0.8000000 1.263158 4
## [15788] {featur,
## network,
## work} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15789] {represent,
## work,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [15790] {show,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [15791] {represent,
## show,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15792] {represent,
## work,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15793] {propos,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15794] {represent,
## propos,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15795] {represent,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [15796] {model,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15797] {model,
## represent,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15798] {represent,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15799] {featur,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15800] {featur,
## represent,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15801] {represent,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [15802] {network,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15803] {network,
## represent,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15804] {show,
## work,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [15805] {network,
## work,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [15806] {network,
## show,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15807] {network,
## show,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [15808] {propos,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [15809] {model,
## work,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15810] {model,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [15811] {propos,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15812] {featur,
## work,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15813] {featur,
## propos,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15814] {propos,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [15815] {network,
## work,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15816] {network,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [15817] {model,
## work,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [15818] {featur,
## work,
## learn} => {model} 0.1333333 1.0000000 1.875000 4
## [15819] {featur,
## model,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [15820] {model,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [15821] {network,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [15822] {model,
## network,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [15823] {model,
## network,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [15824] {featur,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [15825] {network,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15826] {represent,
## show,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [15827] {represent,
## propos,
## work} => {show} 0.1000000 0.7500000 1.406250 3
## [15828] {show,
## propos,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15829] {represent,
## show,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15830] {network,
## represent,
## work} => {show} 0.1000000 0.7500000 1.406250 3
## [15831] {network,
## show,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15832] {model,
## represent,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [15833] {featur,
## represent,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [15834] {featur,
## model,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [15835] {featur,
## represent,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15836] {network,
## represent,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [15837] {show,
## propos,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15838] {network,
## show,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [15839] {network,
## show,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [15840] {model,
## propos,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [15841] {featur,
## propos,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [15842] {featur,
## model,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [15843] {featur,
## propos,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15844] {featur,
## model,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [15845] {model,
## network,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [15846] {data,
## dataset,
## perform} => {represent} 0.1000000 1.0000000 2.000000 3
## [15847] {data,
## represent,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15848] {represent,
## dataset,
## perform} => {data} 0.1000000 0.7500000 1.730769 3
## [15849] {data,
## dataset,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [15850] {data,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [15851] {data,
## perform,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [15852] {data,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [15853] {data,
## model,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [15854] {data,
## perform,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [15855] {data,
## represent,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [15856] {data,
## perform,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [15857] {data,
## represent,
## perform} => {featur} 0.1333333 1.0000000 1.875000 4
## [15858] {data,
## featur,
## perform} => {represent} 0.1333333 0.8000000 1.600000 4
## [15859] {featur,
## represent,
## perform} => {data} 0.1333333 0.8000000 1.846154 4
## [15860] {data,
## perform,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [15861] {data,
## model,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [15862] {data,
## perform,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [15863] {data,
## featur,
## perform} => {propos} 0.1333333 0.8000000 1.600000 4
## [15864] {data,
## model,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [15865] {dataset,
## perform,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15866] {represent,
## dataset,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [15867] {represent,
## perform,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15868] {dataset,
## perform,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15869] {dataset,
## perform,
## learn} => {model} 0.1333333 1.0000000 1.875000 4
## [15870] {model,
## dataset,
## perform} => {learn} 0.1333333 0.8000000 1.846154 4
## [15871] {model,
## perform,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15872] {dataset,
## perform,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15873] {represent,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [15874] {represent,
## dataset,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [15875] {model,
## represent,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15876] {model,
## represent,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [15877] {represent,
## dataset,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [15878] {show,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [15879] {show,
## dataset,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [15880] {model,
## show,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [15881] {model,
## dataset,
## perform} => {propos} 0.1333333 0.8000000 1.600000 4
## [15882] {model,
## perform,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15883] {network,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [15884] {network,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15885] {model,
## dataset,
## perform} => {featur} 0.1333333 0.8000000 1.500000 4
## [15886] {featur,
## model,
## perform} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15887] {network,
## dataset,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [15888] {featur,
## network,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15889] {represent,
## perform,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [15890] {perform,
## propos,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [15891] {represent,
## perform,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [15892] {model,
## represent,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [15893] {represent,
## perform,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15894] {show,
## perform,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15895] {show,
## perform,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [15896] {show,
## perform,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15897] {featur,
## show,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [15898] {perform,
## propos,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [15899] {model,
## perform,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [15900] {model,
## perform,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [15901] {perform,
## propos,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [15902] {featur,
## perform,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [15903] {model,
## perform,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [15904] {featur,
## perform,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [15905] {featur,
## model,
## perform} => {learn} 0.1333333 0.8000000 1.846154 4
## [15906] {represent,
## show,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [15907] {model,
## represent,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [15908] {model,
## represent,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [15909] {featur,
## represent,
## perform} => {propos} 0.1333333 0.8000000 1.600000 4
## [15910] {model,
## show,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [15911] {featur,
## show,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [15912] {featur,
## show,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [15913] {featur,
## show,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [15914] {model,
## perform,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [15915] {featur,
## model,
## perform} => {propos} 0.1333333 0.8000000 1.600000 4
## [15916] {data,
## dataset,
## learn} => {represent} 0.1666667 0.8333333 1.666667 5
## [15917] {data,
## represent,
## dataset} => {learn} 0.1666667 0.8333333 1.923077 5
## [15918] {data,
## represent,
## learn} => {dataset} 0.1666667 0.8333333 1.923077 5
## [15919] {represent,
## dataset,
## learn} => {data} 0.1666667 0.8333333 1.923077 5
## [15920] {data,
## show,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [15921] {data,
## dataset,
## learn} => {propos} 0.1666667 0.8333333 1.666667 5
## [15922] {data,
## dataset,
## propos} => {learn} 0.1666667 1.0000000 2.307692 5
## [15923] {data,
## propos,
## learn} => {dataset} 0.1666667 0.8333333 1.923077 5
## [15924] {dataset,
## propos,
## learn} => {data} 0.1666667 0.8333333 1.923077 5
## [15925] {data,
## model,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [15926] {data,
## dataset,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [15927] {data,
## featur,
## dataset} => {learn} 0.1666667 0.8333333 1.923077 5
## [15928] {data,
## featur,
## learn} => {dataset} 0.1666667 0.7142857 1.648352 5
## [15929] {featur,
## dataset,
## learn} => {data} 0.1666667 0.8333333 1.923077 5
## [15930] {data,
## network,
## dataset} => {learn} 0.1333333 0.8000000 1.846154 4
## [15931] {data,
## network,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [15932] {network,
## dataset,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [15933] {data,
## show,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [15934] {represent,
## show,
## dataset} => {data} 0.1000000 0.7500000 1.730769 3
## [15935] {data,
## dataset,
## propos} => {represent} 0.1333333 0.8000000 1.600000 4
## [15936] {data,
## represent,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15937] {represent,
## dataset,
## propos} => {data} 0.1333333 0.8000000 1.846154 4
## [15938] {data,
## model,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [15939] {model,
## represent,
## dataset} => {data} 0.1000000 0.7500000 1.730769 3
## [15940] {data,
## represent,
## dataset} => {featur} 0.1666667 0.8333333 1.562500 5
## [15941] {data,
## featur,
## dataset} => {represent} 0.1666667 0.8333333 1.666667 5
## [15942] {featur,
## represent,
## dataset} => {data} 0.1666667 1.0000000 2.307692 5
## [15943] {data,
## network,
## dataset} => {represent} 0.1333333 0.8000000 1.600000 4
## [15944] {data,
## network,
## represent} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15945] {network,
## represent,
## dataset} => {data} 0.1333333 1.0000000 2.307692 4
## [15946] {data,
## show,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [15947] {data,
## network,
## show} => {dataset} 0.1000000 0.7500000 1.730769 3
## [15948] {data,
## dataset,
## propos} => {model} 0.1333333 0.8000000 1.500000 4
## [15949] {data,
## model,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [15950] {data,
## model,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15951] {data,
## dataset,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [15952] {data,
## network,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [15953] {data,
## model,
## dataset} => {featur} 0.1333333 1.0000000 1.875000 4
## [15954] {data,
## network,
## dataset} => {featur} 0.1333333 0.8000000 1.500000 4
## [15955] {data,
## featur,
## network} => {dataset} 0.1333333 0.8000000 1.846154 4
## [15956] {data,
## show,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [15957] {data,
## represent,
## show} => {learn} 0.1333333 0.8000000 1.846154 4
## [15958] {data,
## represent,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [15959] {data,
## model,
## represent} => {learn} 0.1333333 0.8000000 1.846154 4
## [15960] {data,
## represent,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [15961] {data,
## featur,
## learn} => {represent} 0.1666667 0.7142857 1.428571 5
## [15962] {data,
## network,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [15963] {data,
## show,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [15964] {data,
## show,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [15965] {data,
## featur,
## show} => {learn} 0.1333333 0.8000000 1.846154 4
## [15966] {data,
## network,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [15967] {data,
## network,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [15968] {network,
## show,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15969] {data,
## propos,
## learn} => {model} 0.1666667 0.8333333 1.562500 5
## [15970] {data,
## model,
## learn} => {propos} 0.1666667 0.8333333 1.666667 5
## [15971] {data,
## model,
## propos} => {learn} 0.1666667 1.0000000 2.307692 5
## [15972] {model,
## propos,
## learn} => {data} 0.1666667 0.8333333 1.923077 5
## [15973] {data,
## propos,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [15974] {data,
## featur,
## learn} => {propos} 0.1666667 0.7142857 1.428571 5
## [15975] {data,
## featur,
## propos} => {learn} 0.1666667 0.8333333 1.923077 5
## [15976] {featur,
## propos,
## learn} => {data} 0.1666667 0.7142857 1.648352 5
## [15977] {data,
## network,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [15978] {data,
## network,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [15979] {network,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [15980] {data,
## model,
## learn} => {featur} 0.2000000 1.0000000 1.875000 6
## [15981] {data,
## featur,
## learn} => {model} 0.2000000 0.8571429 1.607143 6
## [15982] {data,
## featur,
## model} => {learn} 0.2000000 0.8571429 1.978022 6
## [15983] {featur,
## model,
## learn} => {data} 0.2000000 0.7500000 1.730769 6
## [15984] {data,
## network,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [15985] {data,
## represent,
## show} => {featur} 0.1333333 0.8000000 1.500000 4
## [15986] {data,
## featur,
## show} => {represent} 0.1333333 0.8000000 1.600000 4
## [15987] {data,
## represent,
## show} => {network} 0.1333333 0.8000000 1.263158 4
## [15988] {data,
## network,
## represent} => {show} 0.1333333 0.8000000 1.500000 4
## [15989] {data,
## network,
## show} => {represent} 0.1333333 1.0000000 2.000000 4
## [15990] {model,
## represent,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [15991] {data,
## represent,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [15992] {data,
## model,
## represent} => {featur} 0.1666667 1.0000000 1.875000 5
## [15993] {data,
## featur,
## model} => {represent} 0.1666667 0.7142857 1.428571 5
## [15994] {featur,
## model,
## represent} => {data} 0.1666667 0.8333333 1.923077 5
## [15995] {data,
## network,
## represent} => {featur} 0.1333333 0.8000000 1.500000 4
## [15996] {data,
## featur,
## network} => {represent} 0.1333333 0.8000000 1.600000 4
## [15997] {data,
## model,
## show} => {featur} 0.1333333 0.8000000 1.500000 4
## [15998] {data,
## featur,
## show} => {model} 0.1333333 0.8000000 1.500000 4
## [15999] {data,
## network,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [16000] {data,
## model,
## propos} => {featur} 0.1666667 1.0000000 1.875000 5
## [16001] {data,
## featur,
## propos} => {model} 0.1666667 0.8333333 1.562500 5
## [16002] {data,
## featur,
## model} => {propos} 0.1666667 0.7142857 1.428571 5
## [16003] {featur,
## model,
## propos} => {data} 0.1666667 0.8333333 1.923077 5
## [16004] {data,
## model,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [16005] {show,
## dataset,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [16006] {represent,
## show,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [16007] {represent,
## dataset,
## learn} => {propos} 0.1666667 0.8333333 1.666667 5
## [16008] {dataset,
## propos,
## learn} => {represent} 0.1666667 0.8333333 1.666667 5
## [16009] {represent,
## dataset,
## propos} => {learn} 0.1666667 1.0000000 2.307692 5
## [16010] {represent,
## propos,
## learn} => {dataset} 0.1666667 0.7142857 1.648352 5
## [16011] {model,
## represent,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [16012] {featur,
## represent,
## dataset} => {learn} 0.1333333 0.8000000 1.846154 4
## [16013] {network,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [16014] {network,
## represent,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [16015] {model,
## show,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [16016] {featur,
## show,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [16017] {network,
## dataset,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [16018] {network,
## show,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [16019] {dataset,
## propos,
## learn} => {model} 0.1666667 0.8333333 1.562500 5
## [16020] {model,
## dataset,
## learn} => {propos} 0.1666667 0.8333333 1.666667 5
## [16021] {model,
## dataset,
## propos} => {learn} 0.1666667 0.7142857 1.648352 5
## [16022] {model,
## propos,
## learn} => {dataset} 0.1666667 0.8333333 1.923077 5
## [16023] {network,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [16024] {network,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [16025] {model,
## dataset,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [16026] {featur,
## dataset,
## learn} => {model} 0.1666667 0.8333333 1.562500 5
## [16027] {featur,
## model,
## dataset} => {learn} 0.1666667 0.8333333 1.923077 5
## [16028] {network,
## dataset,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [16029] {represent,
## show,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [16030] {represent,
## show,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [16031] {network,
## represent,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [16032] {represent,
## dataset,
## propos} => {model} 0.1333333 0.8000000 1.500000 4
## [16033] {model,
## represent,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [16034] {model,
## represent,
## propos} => {dataset} 0.1333333 1.0000000 2.307692 4
## [16035] {model,
## represent,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [16036] {network,
## represent,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [16037] {model,
## show,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [16038] {model,
## show,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [16039] {show,
## dataset,
## propos} => {network} 0.1333333 0.8000000 1.263158 4
## [16040] {network,
## show,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [16041] {network,
## show,
## propos} => {dataset} 0.1333333 1.0000000 2.307692 4
## [16042] {model,
## show,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [16043] {featur,
## show,
## dataset} => {model} 0.1000000 0.7500000 1.406250 3
## [16044] {featur,
## show,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [16045] {model,
## dataset,
## propos} => {featur} 0.1666667 0.7142857 1.339286 5
## [16046] {featur,
## dataset,
## propos} => {model} 0.1666667 0.8333333 1.562500 5
## [16047] {featur,
## model,
## dataset} => {propos} 0.1666667 0.8333333 1.666667 5
## [16048] {featur,
## model,
## propos} => {dataset} 0.1666667 0.8333333 1.923077 5
## [16049] {model,
## network,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [16050] {model,
## network,
## propos} => {dataset} 0.1333333 1.0000000 2.307692 4
## [16051] {featur,
## network,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [16052] {model,
## network,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [16053] {show,
## propos,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [16054] {represent,
## show,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [16055] {represent,
## show,
## learn} => {featur} 0.1666667 0.7142857 1.339286 5
## [16056] {featur,
## show,
## learn} => {represent} 0.1666667 0.7142857 1.428571 5
## [16057] {featur,
## represent,
## show} => {learn} 0.1666667 0.8333333 1.923077 5
## [16058] {network,
## represent,
## learn} => {show} 0.1333333 0.8000000 1.500000 4
## [16059] {network,
## show,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [16060] {model,
## represent,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [16061] {represent,
## propos,
## learn} => {featur} 0.1666667 0.7142857 1.339286 5
## [16062] {featur,
## propos,
## learn} => {represent} 0.1666667 0.7142857 1.428571 5
## [16063] {featur,
## represent,
## propos} => {learn} 0.1666667 0.8333333 1.923077 5
## [16064] {network,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [16065] {network,
## represent,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [16066] {model,
## represent,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [16067] {featur,
## model,
## represent} => {learn} 0.1666667 0.8333333 1.923077 5
## [16068] {network,
## represent,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [16069] {featur,
## network,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [16070] {model,
## show,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [16071] {featur,
## show,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [16072] {model,
## show,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [16073] {featur,
## show,
## learn} => {model} 0.1666667 0.7142857 1.339286 5
## [16074] {featur,
## model,
## show} => {learn} 0.1666667 0.7142857 1.648352 5
## [16075] {network,
## show,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [16076] {model,
## propos,
## learn} => {featur} 0.1666667 0.8333333 1.562500 5
## [16077] {featur,
## propos,
## learn} => {model} 0.1666667 0.7142857 1.339286 5
## [16078] {featur,
## model,
## propos} => {learn} 0.1666667 0.8333333 1.923077 5
## [16079] {network,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [16080] {model,
## network,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [16081] {model,
## network,
## represent} => {show} 0.1333333 1.0000000 1.875000 4
## [16082] {model,
## network,
## show} => {represent} 0.1333333 0.8000000 1.600000 4
## [16083] {featur,
## network,
## show} => {represent} 0.1333333 0.8000000 1.600000 4
## [16084] {model,
## represent,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [16085] {model,
## network,
## represent} => {featur} 0.1000000 0.7500000 1.406250 3
## [16086] {model,
## show,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [16087] {featur,
## show,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [16088] {model,
## network,
## show} => {featur} 0.1333333 0.8000000 1.500000 4
## [16089] {featur,
## network,
## show} => {model} 0.1333333 0.8000000 1.500000 4
## [16090] {featur,
## model,
## network} => {show} 0.1333333 0.8000000 1.500000 4
## [16091] {model,
## network,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [16092] {data,
## infer,
## make,
## paper} => {model} 0.1000000 1.0000000 1.875000 3
## [16093] {infer,
## make,
## model,
## paper} => {data} 0.1000000 1.0000000 2.307692 3
## [16094] {data,
## infer,
## make,
## model} => {paper} 0.1000000 1.0000000 3.000000 3
## [16095] {data,
## infer,
## model,
## paper} => {make} 0.1000000 1.0000000 3.333333 3
## [16096] {data,
## make,
## model,
## paper} => {infer} 0.1000000 1.0000000 10.000000 3
## [16097] {approach,
## dataset,
## larger,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [16098] {approach,
## larger,
## propos,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16099] {dataset,
## larger,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [16100] {approach,
## dataset,
## larger,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [16101] {approach,
## dataset,
## propos,
## result} => {larger} 0.1000000 0.7500000 7.500000 3
## [16102] {approach,
## dataset,
## larger,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [16103] {approach,
## model,
## larger,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16104] {model,
## dataset,
## larger,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [16105] {approach,
## model,
## dataset,
## larger} => {result} 0.1000000 1.0000000 3.000000 3
## [16106] {approach,
## model,
## dataset,
## result} => {larger} 0.1000000 1.0000000 10.000000 3
## [16107] {approach,
## larger,
## propos,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [16108] {approach,
## model,
## larger,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [16109] {model,
## larger,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [16110] {approach,
## model,
## larger,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [16111] {approach,
## model,
## propos,
## result} => {larger} 0.1000000 1.0000000 10.000000 3
## [16112] {dataset,
## larger,
## propos,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [16113] {model,
## dataset,
## larger,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [16114] {model,
## larger,
## propos,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16115] {model,
## dataset,
## larger,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [16116] {model,
## dataset,
## propos,
## result} => {larger} 0.1000000 1.0000000 10.000000 3
## [16117] {approach,
## dataset,
## larger,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [16118] {approach,
## model,
## dataset,
## larger} => {propos} 0.1000000 1.0000000 2.000000 3
## [16119] {approach,
## model,
## larger,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16120] {model,
## dataset,
## larger,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [16121] {recognit,
## challeng,
## face,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [16122] {train,
## challeng,
## face,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [16123] {train,
## recognit,
## face,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [16124] {train,
## recognit,
## challeng,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [16125] {train,
## recognit,
## challeng,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [16126] {recognit,
## challeng,
## face,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [16127] {represent,
## challeng,
## face,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [16128] {represent,
## recognit,
## face,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [16129] {represent,
## recognit,
## challeng,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [16130] {represent,
## recognit,
## challeng,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [16131] {train,
## challeng,
## face,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [16132] {represent,
## challeng,
## face,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [16133] {represent,
## train,
## face,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [16134] {represent,
## train,
## challeng,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [16135] {represent,
## train,
## challeng,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [16136] {train,
## recognit,
## face,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [16137] {represent,
## recognit,
## face,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [16138] {represent,
## train,
## face,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [16139] {represent,
## train,
## recognit,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [16140] {represent,
## train,
## recognit,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [16141] {train,
## recognit,
## challeng,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [16142] {represent,
## recognit,
## challeng,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [16143] {represent,
## train,
## challeng,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [16144] {represent,
## train,
## recognit,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [16145] {represent,
## train,
## recognit,
## challeng} => {ident} 0.1000000 1.0000000 10.000000 3
## [16146] {make,
## perform,
## problem,
## standard} => {model} 0.1000000 1.0000000 1.875000 3
## [16147] {make,
## model,
## problem,
## standard} => {perform} 0.1000000 1.0000000 2.142857 3
## [16148] {make,
## model,
## perform,
## standard} => {problem} 0.1000000 1.0000000 3.333333 3
## [16149] {model,
## perform,
## problem,
## standard} => {make} 0.1000000 1.0000000 3.333333 3
## [16150] {make,
## model,
## perform,
## problem} => {standard} 0.1000000 0.7500000 7.500000 3
## [16151] {improv,
## perform,
## shallow,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16152] {dataset,
## improv,
## shallow,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [16153] {dataset,
## perform,
## shallow,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [16154] {dataset,
## improv,
## perform,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [16155] {dataset,
## improv,
## perform,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [16156] {improv,
## perform,
## shallow,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [16157] {featur,
## improv,
## shallow,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [16158] {featur,
## perform,
## shallow,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [16159] {featur,
## improv,
## perform,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [16160] {featur,
## improv,
## perform,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [16161] {dataset,
## improv,
## shallow,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [16162] {featur,
## improv,
## shallow,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16163] {featur,
## dataset,
## shallow,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [16164] {featur,
## dataset,
## improv,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [16165] {featur,
## dataset,
## improv,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [16166] {dataset,
## perform,
## shallow,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [16167] {featur,
## perform,
## shallow,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16168] {featur,
## dataset,
## shallow,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [16169] {featur,
## dataset,
## perform,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [16170] {featur,
## dataset,
## perform,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [16171] {dataset,
## improv,
## perform,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [16172] {featur,
## improv,
## perform,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16173] {featur,
## dataset,
## improv,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [16174] {featur,
## dataset,
## perform,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [16175] {featur,
## dataset,
## improv,
## perform} => {tradit} 0.1000000 0.7500000 7.500000 3
## [16176] {model,
## perform,
## learn,
## discrimin} => {featur} 0.1000000 1.0000000 1.875000 3
## [16177] {featur,
## perform,
## learn,
## discrimin} => {model} 0.1000000 1.0000000 1.875000 3
## [16178] {featur,
## model,
## perform,
## discrimin} => {learn} 0.1000000 1.0000000 2.307692 3
## [16179] {featur,
## model,
## learn,
## discrimin} => {perform} 0.1000000 1.0000000 2.142857 3
## [16180] {featur,
## model,
## perform,
## learn} => {discrimin} 0.1000000 0.7500000 7.500000 3
## [16181] {advantag,
## approach,
## classif,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [16182] {advantag,
## classif,
## featur,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [16183] {advantag,
## approach,
## classif,
## featur} => {method} 0.1000000 1.0000000 2.727273 3
## [16184] {advantag,
## approach,
## featur,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [16185] {approach,
## classif,
## featur,
## method} => {advantag} 0.1000000 0.7500000 7.500000 3
## [16186] {advantag,
## approach,
## classif,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [16187] {advantag,
## classif,
## method,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [16188] {advantag,
## approach,
## classif,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [16189] {advantag,
## approach,
## method,
## network} => {classif} 0.1000000 1.0000000 3.750000 3
## [16190] {approach,
## classif,
## method,
## network} => {advantag} 0.1000000 0.7500000 7.500000 3
## [16191] {advantag,
## classif,
## featur,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [16192] {advantag,
## classif,
## method,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [16193] {advantag,
## classif,
## featur,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [16194] {advantag,
## featur,
## method,
## network} => {classif} 0.1000000 1.0000000 3.750000 3
## [16195] {classif,
## featur,
## method,
## network} => {advantag} 0.1000000 1.0000000 10.000000 3
## [16196] {advantag,
## approach,
## classif,
## featur} => {network} 0.1000000 1.0000000 1.578947 3
## [16197] {advantag,
## approach,
## classif,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [16198] {advantag,
## classif,
## featur,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [16199] {advantag,
## approach,
## featur,
## network} => {classif} 0.1000000 1.0000000 3.750000 3
## [16200] {approach,
## classif,
## featur,
## network} => {advantag} 0.1000000 1.0000000 10.000000 3
## [16201] {advantag,
## approach,
## featur,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [16202] {advantag,
## approach,
## method,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [16203] {advantag,
## featur,
## method,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [16204] {advantag,
## approach,
## featur,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [16205] {approach,
## featur,
## method,
## network} => {advantag} 0.1000000 0.7500000 7.500000 3
## [16206] {show,
## perform,
## imag,
## local} => {propos} 0.1000000 1.0000000 2.000000 3
## [16207] {perform,
## propos,
## imag,
## local} => {show} 0.1000000 1.0000000 1.875000 3
## [16208] {show,
## propos,
## imag,
## local} => {perform} 0.1000000 1.0000000 2.142857 3
## [16209] {show,
## perform,
## propos,
## local} => {imag} 0.1000000 1.0000000 6.000000 3
## [16210] {show,
## perform,
## propos,
## imag} => {local} 0.1000000 1.0000000 10.000000 3
## [16211] {classif,
## machin,
## learn,
## field} => {featur} 0.1000000 1.0000000 1.875000 3
## [16212] {classif,
## featur,
## machin,
## field} => {learn} 0.1000000 1.0000000 2.307692 3
## [16213] {featur,
## machin,
## learn,
## field} => {classif} 0.1000000 1.0000000 3.750000 3
## [16214] {classif,
## featur,
## learn,
## field} => {machin} 0.1000000 1.0000000 4.285714 3
## [16215] {classif,
## featur,
## machin,
## learn} => {field} 0.1000000 1.0000000 10.000000 3
## [16216] {approach,
## show,
## dataset,
## common} => {propos} 0.1000000 1.0000000 2.000000 3
## [16217] {approach,
## dataset,
## propos,
## common} => {show} 0.1000000 1.0000000 1.875000 3
## [16218] {approach,
## show,
## propos,
## common} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16219] {show,
## dataset,
## propos,
## common} => {approach} 0.1000000 1.0000000 2.500000 3
## [16220] {data,
## dataset,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [16221] {data,
## dataset,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [16222] {data,
## propos,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16223] {dataset,
## propos,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [16224] {data,
## dataset,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [16225] {data,
## model,
## dataset,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [16226] {data,
## model,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16227] {model,
## dataset,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [16228] {data,
## model,
## dataset,
## learn} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [16229] {data,
## dataset,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [16230] {data,
## featur,
## dataset,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [16231] {data,
## featur,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16232] {featur,
## dataset,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [16233] {data,
## dataset,
## propos,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [16234] {data,
## model,
## dataset,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [16235] {data,
## model,
## propos,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16236] {model,
## dataset,
## propos,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [16237] {data,
## model,
## dataset,
## propos} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [16238] {data,
## dataset,
## propos,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [16239] {data,
## featur,
## dataset,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [16240] {data,
## featur,
## propos,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16241] {featur,
## dataset,
## propos,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [16242] {data,
## featur,
## dataset,
## propos} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [16243] {data,
## model,
## dataset,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [16244] {data,
## featur,
## dataset,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [16245] {data,
## featur,
## model,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16246] {featur,
## model,
## dataset,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [16247] {data,
## featur,
## model,
## dataset} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [16248] {data,
## propos,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [16249] {data,
## model,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [16250] {data,
## model,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [16251] {model,
## propos,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [16252] {data,
## propos,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [16253] {data,
## featur,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [16254] {data,
## featur,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [16255] {featur,
## propos,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [16256] {data,
## model,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [16257] {data,
## featur,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [16258] {data,
## featur,
## model,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [16259] {featur,
## model,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [16260] {data,
## model,
## propos,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [16261] {data,
## featur,
## propos,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [16262] {data,
## featur,
## model,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [16263] {featur,
## model,
## propos,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [16264] {dataset,
## propos,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [16265] {model,
## dataset,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [16266] {model,
## dataset,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [16267] {model,
## propos,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16268] {dataset,
## propos,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [16269] {featur,
## dataset,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [16270] {featur,
## dataset,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [16271] {featur,
## propos,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16272] {featur,
## dataset,
## propos,
## learn} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [16273] {model,
## dataset,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [16274] {featur,
## dataset,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [16275] {featur,
## model,
## dataset,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [16276] {featur,
## model,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16277] {model,
## dataset,
## propos,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [16278] {featur,
## dataset,
## propos,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [16279] {featur,
## model,
## dataset,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [16280] {featur,
## model,
## propos,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16281] {model,
## propos,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [16282] {featur,
## propos,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [16283] {featur,
## model,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [16284] {featur,
## model,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [16285] {compon,
## dataset,
## experi,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [16286] {model,
## compon,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [16287] {model,
## compon,
## experi,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16288] {model,
## compon,
## dataset,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [16289] {model,
## dataset,
## experi,
## propos} => {compon} 0.1000000 1.0000000 10.000000 3
## [16290] {train,
## neural,
## provid,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16291] {train,
## dataset,
## neural,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [16292] {dataset,
## neural,
## provid,
## work} => {train} 0.1000000 1.0000000 2.500000 3
## [16293] {train,
## dataset,
## provid,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [16294] {train,
## dataset,
## neural,
## work} => {provid} 0.1000000 1.0000000 10.000000 3
## [16295] {train,
## neural,
## provid,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [16296] {network,
## train,
## neural,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [16297] {network,
## neural,
## provid,
## work} => {train} 0.1000000 1.0000000 2.500000 3
## [16298] {network,
## train,
## provid,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [16299] {network,
## train,
## neural,
## work} => {provid} 0.1000000 1.0000000 10.000000 3
## [16300] {train,
## dataset,
## neural,
## provid} => {network} 0.1000000 1.0000000 1.578947 3
## [16301] {network,
## train,
## neural,
## provid} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16302] {network,
## dataset,
## neural,
## provid} => {train} 0.1000000 1.0000000 2.500000 3
## [16303] {network,
## train,
## dataset,
## provid} => {neural} 0.1000000 1.0000000 3.000000 3
## [16304] {network,
## train,
## dataset,
## neural} => {provid} 0.1000000 0.7500000 7.500000 3
## [16305] {dataset,
## neural,
## provid,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [16306] {network,
## neural,
## provid,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16307] {network,
## dataset,
## neural,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [16308] {network,
## dataset,
## provid,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [16309] {network,
## dataset,
## neural,
## work} => {provid} 0.1000000 1.0000000 10.000000 3
## [16310] {train,
## dataset,
## provid,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [16311] {network,
## train,
## provid,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16312] {network,
## train,
## dataset,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [16313] {network,
## dataset,
## provid,
## work} => {train} 0.1000000 1.0000000 2.500000 3
## [16314] {network,
## train,
## dataset,
## work} => {provid} 0.1000000 1.0000000 10.000000 3
## [16315] {classif,
## object,
## propos,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [16316] {classif,
## model,
## object,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [16317] {model,
## object,
## propos,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [16318] {classif,
## model,
## propos,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [16319] {classif,
## model,
## object,
## propos} => {test} 0.1000000 1.0000000 10.000000 3
## [16320] {classif,
## object,
## propos,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [16321] {classif,
## featur,
## object,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [16322] {featur,
## object,
## propos,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [16323] {classif,
## featur,
## propos,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [16324] {classif,
## featur,
## object,
## propos} => {test} 0.1000000 0.7500000 7.500000 3
## [16325] {classif,
## model,
## object,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [16326] {classif,
## featur,
## object,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [16327] {featur,
## model,
## object,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [16328] {classif,
## featur,
## model,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [16329] {classif,
## featur,
## model,
## object} => {test} 0.1000000 1.0000000 10.000000 3
## [16330] {model,
## object,
## propos,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [16331] {featur,
## object,
## propos,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [16332] {featur,
## model,
## object,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [16333] {featur,
## model,
## propos,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [16334] {featur,
## model,
## object,
## propos} => {test} 0.1000000 0.7500000 7.500000 3
## [16335] {classif,
## model,
## propos,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [16336] {classif,
## featur,
## propos,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [16337] {classif,
## featur,
## model,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [16338] {featur,
## model,
## propos,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [16339] {classif,
## featur,
## model,
## propos} => {test} 0.1000000 1.0000000 10.000000 3
## [16340] {extract,
## general,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [16341] {show,
## extract,
## general,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [16342] {show,
## extract,
## general,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [16343] {show,
## extract,
## recognit,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [16344] {show,
## general,
## recognit,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [16345] {extract,
## general,
## recognit,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [16346] {featur,
## extract,
## general,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [16347] {featur,
## extract,
## general,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [16348] {featur,
## extract,
## recognit,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [16349] {featur,
## general,
## recognit,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [16350] {show,
## extract,
## general,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [16351] {featur,
## extract,
## general,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [16352] {featur,
## show,
## extract,
## general} => {recognit} 0.1000000 1.0000000 3.333333 3
## [16353] {featur,
## show,
## extract,
## recognit} => {general} 0.1000000 1.0000000 5.000000 3
## [16354] {featur,
## show,
## general,
## recognit} => {extract} 0.1000000 1.0000000 7.500000 3
## [16355] {show,
## extract,
## general,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [16356] {featur,
## extract,
## general,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [16357] {featur,
## show,
## extract,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [16358] {featur,
## show,
## extract,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [16359] {featur,
## show,
## general,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [16360] {show,
## extract,
## recognit,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [16361] {featur,
## extract,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [16362] {featur,
## show,
## extract,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [16363] {featur,
## show,
## extract,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [16364] {featur,
## show,
## recognit,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [16365] {show,
## algorithm,
## extract,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [16366] {model,
## algorithm,
## extract,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [16367] {model,
## show,
## extract,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16368] {model,
## show,
## algorithm,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [16369] {model,
## show,
## algorithm,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [16370] {appli,
## dataset,
## propos,
## report} => {network} 0.1000000 1.0000000 1.578947 3
## [16371] {network,
## appli,
## dataset,
## report} => {propos} 0.1000000 1.0000000 2.000000 3
## [16372] {network,
## appli,
## propos,
## report} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16373] {network,
## dataset,
## propos,
## report} => {appli} 0.1000000 1.0000000 5.000000 3
## [16374] {network,
## appli,
## dataset,
## propos} => {report} 0.1000000 0.7500000 5.625000 3
## [16375] {data,
## employ,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16376] {data,
## employ,
## featur,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16377] {employ,
## featur,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16378] {data,
## employ,
## featur,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [16379] {approach,
## achiev,
## propos,
## abil} => {network} 0.1000000 1.0000000 1.578947 3
## [16380] {approach,
## network,
## achiev,
## abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [16381] {network,
## achiev,
## propos,
## abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [16382] {approach,
## network,
## propos,
## abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [16383] {approach,
## network,
## achiev,
## propos} => {abil} 0.1000000 0.7500000 7.500000 3
## [16384] {data,
## show,
## algorithm,
## structur} => {model} 0.1000000 1.0000000 1.875000 3
## [16385] {data,
## model,
## algorithm,
## structur} => {show} 0.1000000 1.0000000 1.875000 3
## [16386] {model,
## show,
## algorithm,
## structur} => {data} 0.1000000 1.0000000 2.307692 3
## [16387] {data,
## model,
## show,
## structur} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16388] {data,
## model,
## show,
## algorithm} => {structur} 0.1000000 1.0000000 7.500000 3
## [16389] {show,
## propos,
## learn,
## inform} => {model} 0.1000000 1.0000000 1.875000 3
## [16390] {model,
## show,
## learn,
## inform} => {propos} 0.1000000 1.0000000 2.000000 3
## [16391] {model,
## propos,
## learn,
## inform} => {show} 0.1000000 1.0000000 1.875000 3
## [16392] {model,
## show,
## propos,
## inform} => {learn} 0.1000000 1.0000000 2.307692 3
## [16393] {model,
## show,
## propos,
## learn} => {inform} 0.1000000 1.0000000 10.000000 3
## [16394] {architectur,
## convolut,
## effici,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [16395] {architectur,
## perform,
## convolut,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [16396] {perform,
## convolut,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [16397] {architectur,
## perform,
## convolut,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [16398] {architectur,
## perform,
## effici,
## work} => {convolut} 0.1000000 1.0000000 7.500000 3
## [16399] {architectur,
## convolut,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [16400] {network,
## architectur,
## convolut,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [16401] {network,
## convolut,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [16402] {network,
## architectur,
## convolut,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [16403] {network,
## architectur,
## effici,
## work} => {convolut} 0.1000000 0.7500000 5.625000 3
## [16404] {architectur,
## perform,
## convolut,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [16405] {network,
## architectur,
## convolut,
## effici} => {perform} 0.1000000 1.0000000 2.142857 3
## [16406] {network,
## perform,
## convolut,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [16407] {network,
## architectur,
## perform,
## convolut} => {effici} 0.1000000 1.0000000 6.000000 3
## [16408] {network,
## architectur,
## perform,
## effici} => {convolut} 0.1000000 1.0000000 7.500000 3
## [16409] {perform,
## convolut,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [16410] {network,
## convolut,
## effici,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [16411] {network,
## perform,
## convolut,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [16412] {network,
## perform,
## convolut,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [16413] {network,
## perform,
## effici,
## work} => {convolut} 0.1000000 1.0000000 7.500000 3
## [16414] {architectur,
## perform,
## convolut,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [16415] {network,
## architectur,
## convolut,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [16416] {network,
## architectur,
## perform,
## convolut} => {work} 0.1000000 1.0000000 2.500000 3
## [16417] {network,
## perform,
## convolut,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [16418] {network,
## architectur,
## perform,
## work} => {convolut} 0.1000000 0.7500000 5.625000 3
## [16419] {data,
## task,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16420] {task,
## dataset,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [16421] {data,
## dataset,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [16422] {data,
## task,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16423] {data,
## task,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16424] {data,
## task,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [16425] {task,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [16426] {data,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [16427] {data,
## task,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16428] {data,
## task,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16429] {data,
## task,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [16430] {featur,
## task,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [16431] {data,
## featur,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [16432] {data,
## featur,
## task,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16433] {data,
## featur,
## task,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16434] {data,
## task,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [16435] {network,
## task,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [16436] {data,
## network,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [16437] {data,
## network,
## task,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16438] {data,
## network,
## task,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16439] {task,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [16440] {task,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16441] {dataset,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [16442] {task,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16443] {task,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16444] {task,
## dataset,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [16445] {featur,
## task,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16446] {featur,
## dataset,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [16447] {featur,
## task,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16448] {featur,
## task,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16449] {task,
## dataset,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [16450] {network,
## task,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16451] {network,
## dataset,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [16452] {network,
## task,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16453] {network,
## task,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16454] {task,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [16455] {featur,
## task,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [16456] {featur,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [16457] {featur,
## task,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16458] {featur,
## task,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16459] {task,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [16460] {network,
## task,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [16461] {network,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [16462] {network,
## task,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16463] {network,
## task,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16464] {featur,
## task,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [16465] {network,
## task,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [16466] {featur,
## network,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [16467] {featur,
## network,
## task,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16468] {featur,
## network,
## task,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16469] {data,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [16470] {data,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16471] {dataset,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [16472] {data,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16473] {data,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16474] {data,
## dataset,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [16475] {data,
## featur,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16476] {featur,
## dataset,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [16477] {data,
## featur,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16478] {data,
## featur,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16479] {data,
## dataset,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [16480] {data,
## network,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16481] {network,
## dataset,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [16482] {data,
## network,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16483] {data,
## network,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16484] {data,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [16485] {data,
## featur,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [16486] {featur,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [16487] {data,
## featur,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16488] {data,
## featur,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16489] {data,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [16490] {data,
## network,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [16491] {network,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [16492] {data,
## network,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16493] {data,
## network,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16494] {data,
## featur,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [16495] {data,
## network,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [16496] {featur,
## network,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [16497] {data,
## featur,
## network,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16498] {data,
## featur,
## network,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16499] {dataset,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [16500] {featur,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [16501] {featur,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16502] {featur,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16503] {featur,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16504] {dataset,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [16505] {network,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [16506] {network,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16507] {network,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16508] {network,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16509] {featur,
## dataset,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [16510] {network,
## dataset,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [16511] {featur,
## network,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16512] {featur,
## network,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16513] {featur,
## network,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16514] {featur,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [16515] {network,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [16516] {featur,
## network,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [16517] {featur,
## network,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [16518] {featur,
## network,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [16519] {data,
## task,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [16520] {data,
## task,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16521] {task,
## dataset,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [16522] {data,
## dataset,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [16523] {data,
## task,
## dataset,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [16524] {data,
## featur,
## task,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16525] {featur,
## task,
## dataset,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [16526] {data,
## featur,
## dataset,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [16527] {data,
## featur,
## task,
## dataset} => {care} 0.1000000 0.7500000 7.500000 3
## [16528] {data,
## task,
## dataset,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [16529] {data,
## network,
## task,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16530] {network,
## task,
## dataset,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [16531] {data,
## network,
## dataset,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [16532] {data,
## network,
## task,
## dataset} => {care} 0.1000000 0.7500000 7.500000 3
## [16533] {data,
## task,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [16534] {data,
## featur,
## task,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [16535] {featur,
## task,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [16536] {data,
## featur,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [16537] {data,
## task,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [16538] {data,
## network,
## task,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [16539] {network,
## task,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [16540] {data,
## network,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [16541] {data,
## network,
## task,
## learn} => {care} 0.1000000 0.7500000 7.500000 3
## [16542] {data,
## featur,
## task,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [16543] {data,
## network,
## task,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [16544] {featur,
## network,
## task,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [16545] {data,
## featur,
## network,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [16546] {data,
## featur,
## network,
## task} => {care} 0.1000000 0.7500000 7.500000 3
## [16547] {task,
## dataset,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [16548] {featur,
## task,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [16549] {featur,
## task,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16550] {featur,
## dataset,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [16551] {featur,
## task,
## dataset,
## learn} => {care} 0.1000000 0.7500000 7.500000 3
## [16552] {task,
## dataset,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [16553] {network,
## task,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [16554] {network,
## task,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16555] {network,
## dataset,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [16556] {network,
## task,
## dataset,
## learn} => {care} 0.1000000 0.7500000 7.500000 3
## [16557] {featur,
## task,
## dataset,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [16558] {network,
## task,
## dataset,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [16559] {featur,
## network,
## task,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16560] {featur,
## network,
## dataset,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [16561] {featur,
## network,
## task,
## dataset} => {care} 0.1000000 1.0000000 10.000000 3
## [16562] {featur,
## task,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [16563] {network,
## task,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [16564] {featur,
## network,
## task,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [16565] {featur,
## network,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [16566] {featur,
## network,
## task,
## learn} => {care} 0.1000000 0.7500000 7.500000 3
## [16567] {data,
## dataset,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [16568] {data,
## featur,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [16569] {data,
## featur,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16570] {featur,
## dataset,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [16571] {data,
## dataset,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [16572] {data,
## network,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [16573] {data,
## network,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16574] {network,
## dataset,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [16575] {data,
## network,
## dataset,
## learn} => {care} 0.1000000 0.7500000 7.500000 3
## [16576] {data,
## featur,
## dataset,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [16577] {data,
## network,
## dataset,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [16578] {data,
## featur,
## network,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16579] {featur,
## network,
## dataset,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [16580] {data,
## featur,
## network,
## dataset} => {care} 0.1000000 0.7500000 7.500000 3
## [16581] {data,
## featur,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [16582] {data,
## network,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [16583] {data,
## featur,
## network,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [16584] {featur,
## network,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [16585] {data,
## featur,
## network,
## learn} => {care} 0.1000000 1.0000000 10.000000 3
## [16586] {featur,
## dataset,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [16587] {network,
## dataset,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [16588] {featur,
## network,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [16589] {featur,
## network,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16590] {featur,
## network,
## dataset,
## learn} => {care} 0.1000000 1.0000000 10.000000 3
## [16591] {represent,
## dataset,
## learn,
## rate} => {show} 0.1000000 1.0000000 1.875000 3
## [16592] {show,
## dataset,
## learn,
## rate} => {represent} 0.1000000 1.0000000 2.000000 3
## [16593] {represent,
## show,
## dataset,
## rate} => {learn} 0.1000000 1.0000000 2.307692 3
## [16594] {represent,
## show,
## learn,
## rate} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16595] {represent,
## show,
## dataset,
## learn} => {rate} 0.1000000 0.7500000 5.625000 3
## [16596] {approach,
## represent,
## captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [16597] {approach,
## featur,
## captur,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [16598] {approach,
## featur,
## represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [16599] {featur,
## represent,
## captur,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [16600] {approach,
## featur,
## represent,
## learn} => {captur} 0.1000000 0.7500000 7.500000 3
## [16601] {approach,
## represent,
## captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [16602] {approach,
## network,
## captur,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [16603] {approach,
## network,
## represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [16604] {network,
## represent,
## captur,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [16605] {approach,
## network,
## represent,
## learn} => {captur} 0.1000000 0.7500000 7.500000 3
## [16606] {approach,
## featur,
## captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [16607] {approach,
## network,
## captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [16608] {approach,
## featur,
## network,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [16609] {featur,
## network,
## captur,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [16610] {approach,
## featur,
## network,
## learn} => {captur} 0.1000000 1.0000000 10.000000 3
## [16611] {approach,
## featur,
## represent,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [16612] {approach,
## network,
## represent,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [16613] {approach,
## featur,
## network,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [16614] {featur,
## network,
## represent,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [16615] {approach,
## featur,
## network,
## represent} => {captur} 0.1000000 0.7500000 7.500000 3
## [16616] {featur,
## represent,
## captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [16617] {network,
## represent,
## captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [16618] {featur,
## network,
## captur,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [16619] {featur,
## network,
## represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [16620] {featur,
## network,
## represent,
## learn} => {captur} 0.1000000 0.7500000 7.500000 3
## [16621] {approach,
## dataset,
## propos,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [16622] {approach,
## network,
## dataset,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [16623] {approach,
## network,
## propos,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16624] {network,
## dataset,
## propos,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [16625] {process,
## simpl,
## studi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [16626] {network,
## process,
## simpl,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [16627] {network,
## simpl,
## studi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [16628] {network,
## process,
## simpl,
## work} => {studi} 0.1000000 1.0000000 7.500000 3
## [16629] {network,
## process,
## studi,
## work} => {simpl} 0.1000000 1.0000000 10.000000 3
## [16630] {train,
## dataset,
## result,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [16631] {network,
## train,
## result,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16632] {network,
## dataset,
## result,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [16633] {network,
## train,
## dataset,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [16634] {network,
## train,
## dataset,
## result} => {increas} 0.1000000 0.7500000 5.625000 3
## [16635] {boltzmann,
## machin,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16636] {boltzmann,
## restrict,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16637] {boltzmann,
## machin,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16638] {boltzmann,
## machin,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16639] {machin,
## restrict,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16640] {boltzmann,
## machin,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16641] {boltzmann,
## restrict,
## show,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16642] {boltzmann,
## machin,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [16643] {boltzmann,
## machin,
## show,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16644] {machin,
## restrict,
## show,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16645] {boltzmann,
## machin,
## restrict,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16646] {boltzmann,
## model,
## restrict,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16647] {boltzmann,
## machin,
## model,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [16648] {boltzmann,
## machin,
## model,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16649] {machin,
## model,
## restrict,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16650] {boltzmann,
## restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16651] {boltzmann,
## restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16652] {boltzmann,
## restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16653] {boltzmann,
## show,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16654] {restrict,
## show,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16655] {boltzmann,
## restrict,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16656] {boltzmann,
## model,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16657] {boltzmann,
## model,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16658] {boltzmann,
## model,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16659] {model,
## restrict,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16660] {boltzmann,
## restrict,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16661] {boltzmann,
## model,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16662] {boltzmann,
## model,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [16663] {boltzmann,
## model,
## show,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16664] {model,
## restrict,
## show,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16665] {boltzmann,
## machin,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [16666] {boltzmann,
## machin,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [16667] {boltzmann,
## restrict,
## show,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [16668] {boltzmann,
## machin,
## show,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16669] {machin,
## restrict,
## show,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16670] {boltzmann,
## machin,
## restrict,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [16671] {boltzmann,
## machin,
## model,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [16672] {boltzmann,
## model,
## restrict,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [16673] {boltzmann,
## machin,
## model,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16674] {machin,
## model,
## restrict,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16675] {boltzmann,
## machin,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16676] {boltzmann,
## data,
## machin,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16677] {boltzmann,
## data,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16678] {boltzmann,
## data,
## machin,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16679] {data,
## machin,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16680] {boltzmann,
## machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16681] {boltzmann,
## machin,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [16682] {boltzmann,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16683] {boltzmann,
## machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16684] {machin,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16685] {boltzmann,
## machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16686] {boltzmann,
## machin,
## model,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [16687] {boltzmann,
## model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16688] {boltzmann,
## machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16689] {machin,
## model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16690] {boltzmann,
## machin,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16691] {boltzmann,
## featur,
## machin,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16692] {boltzmann,
## featur,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16693] {boltzmann,
## featur,
## machin,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16694] {featur,
## machin,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16695] {boltzmann,
## machin,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [16696] {boltzmann,
## machin,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [16697] {boltzmann,
## restrict,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [16698] {boltzmann,
## machin,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16699] {machin,
## restrict,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16700] {boltzmann,
## machin,
## restrict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [16701] {boltzmann,
## machin,
## model,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [16702] {boltzmann,
## model,
## restrict,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [16703] {boltzmann,
## machin,
## model,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16704] {machin,
## model,
## restrict,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16705] {boltzmann,
## data,
## machin,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16706] {boltzmann,
## machin,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [16707] {boltzmann,
## data,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16708] {boltzmann,
## data,
## machin,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16709] {data,
## machin,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16710] {boltzmann,
## data,
## machin,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [16711] {boltzmann,
## machin,
## model,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [16712] {boltzmann,
## data,
## model,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [16713] {boltzmann,
## data,
## machin,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16714] {data,
## machin,
## model,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16715] {boltzmann,
## data,
## machin,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [16716] {boltzmann,
## featur,
## machin,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [16717] {boltzmann,
## data,
## featur,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [16718] {boltzmann,
## data,
## featur,
## machin} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16719] {data,
## featur,
## machin,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16720] {boltzmann,
## machin,
## restrict,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [16721] {boltzmann,
## machin,
## model,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [16722] {boltzmann,
## model,
## restrict,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [16723] {boltzmann,
## machin,
## model,
## show} => {restrict} 0.1333333 1.0000000 7.500000 4
## [16724] {machin,
## model,
## restrict,
## show} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [16725] {boltzmann,
## machin,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [16726] {boltzmann,
## featur,
## machin,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16727] {boltzmann,
## featur,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16728] {boltzmann,
## featur,
## machin,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16729] {featur,
## machin,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16730] {boltzmann,
## machin,
## model,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [16731] {boltzmann,
## featur,
## machin,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [16732] {boltzmann,
## featur,
## model,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [16733] {boltzmann,
## featur,
## machin,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16734] {featur,
## machin,
## model,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16735] {boltzmann,
## restrict,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [16736] {boltzmann,
## model,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [16737] {boltzmann,
## model,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [16738] {boltzmann,
## model,
## show,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16739] {model,
## restrict,
## show,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16740] {boltzmann,
## data,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16741] {boltzmann,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16742] {boltzmann,
## data,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16743] {boltzmann,
## data,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16744] {data,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16745] {boltzmann,
## data,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16746] {boltzmann,
## model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16747] {boltzmann,
## data,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16748] {boltzmann,
## data,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16749] {data,
## model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16750] {boltzmann,
## data,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16751] {boltzmann,
## featur,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16752] {boltzmann,
## data,
## featur,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16753] {boltzmann,
## data,
## featur,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16754] {data,
## featur,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16755] {boltzmann,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16756] {boltzmann,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16757] {boltzmann,
## model,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [16758] {boltzmann,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16759] {model,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16760] {boltzmann,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16761] {boltzmann,
## featur,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16762] {boltzmann,
## featur,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16763] {boltzmann,
## featur,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16764] {featur,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16765] {boltzmann,
## model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16766] {boltzmann,
## featur,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16767] {boltzmann,
## featur,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16768] {boltzmann,
## featur,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16769] {featur,
## model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16770] {boltzmann,
## restrict,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [16771] {boltzmann,
## model,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [16772] {boltzmann,
## model,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [16773] {boltzmann,
## model,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16774] {model,
## restrict,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16775] {boltzmann,
## data,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16776] {boltzmann,
## data,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16777] {boltzmann,
## model,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [16778] {boltzmann,
## data,
## model,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16779] {data,
## model,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16780] {boltzmann,
## data,
## restrict,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [16781] {boltzmann,
## data,
## featur,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16782] {boltzmann,
## featur,
## restrict,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [16783] {boltzmann,
## data,
## featur,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16784] {data,
## featur,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16785] {boltzmann,
## data,
## model,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [16786] {boltzmann,
## data,
## featur,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [16787] {boltzmann,
## featur,
## model,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [16788] {boltzmann,
## data,
## featur,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16789] {data,
## featur,
## model,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16790] {boltzmann,
## model,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [16791] {boltzmann,
## featur,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16792] {boltzmann,
## featur,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16793] {boltzmann,
## featur,
## model,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16794] {featur,
## model,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16795] {boltzmann,
## machin,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16796] {boltzmann,
## machin,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16797] {boltzmann,
## show,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16798] {boltzmann,
## machin,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16799] {machin,
## show,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16800] {boltzmann,
## machin,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16801] {boltzmann,
## machin,
## model,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16802] {boltzmann,
## model,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16803] {boltzmann,
## machin,
## model,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16804] {machin,
## model,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16805] {boltzmann,
## machin,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16806] {boltzmann,
## machin,
## model,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16807] {boltzmann,
## model,
## show,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16808] {boltzmann,
## machin,
## model,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [16809] {machin,
## model,
## show,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16810] {boltzmann,
## show,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16811] {boltzmann,
## model,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16812] {boltzmann,
## model,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16813] {boltzmann,
## model,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16814] {model,
## show,
## algorithm,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16815] {boltzmann,
## machin,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [16816] {boltzmann,
## machin,
## model,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [16817] {boltzmann,
## machin,
## model,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [16818] {boltzmann,
## model,
## show,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [16819] {machin,
## model,
## show,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16820] {boltzmann,
## data,
## machin,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16821] {boltzmann,
## machin,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16822] {boltzmann,
## data,
## machin,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16823] {boltzmann,
## data,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16824] {data,
## machin,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16825] {boltzmann,
## data,
## machin,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16826] {boltzmann,
## machin,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16827] {boltzmann,
## data,
## machin,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [16828] {boltzmann,
## data,
## model,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16829] {data,
## machin,
## model,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16830] {boltzmann,
## data,
## machin,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16831] {boltzmann,
## featur,
## machin,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16832] {boltzmann,
## data,
## featur,
## machin} => {task} 0.1000000 1.0000000 2.727273 3
## [16833] {boltzmann,
## data,
## featur,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16834] {data,
## featur,
## machin,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16835] {boltzmann,
## machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16836] {boltzmann,
## machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16837] {boltzmann,
## machin,
## model,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [16838] {boltzmann,
## model,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16839] {machin,
## model,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16840] {boltzmann,
## machin,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16841] {boltzmann,
## featur,
## machin,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16842] {boltzmann,
## featur,
## machin,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16843] {boltzmann,
## featur,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16844] {featur,
## machin,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16845] {boltzmann,
## machin,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16846] {boltzmann,
## featur,
## machin,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16847] {boltzmann,
## featur,
## machin,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [16848] {boltzmann,
## featur,
## model,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16849] {featur,
## machin,
## model,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16850] {boltzmann,
## machin,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [16851] {boltzmann,
## machin,
## model,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [16852] {boltzmann,
## machin,
## model,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [16853] {boltzmann,
## model,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [16854] {machin,
## model,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16855] {boltzmann,
## data,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16856] {boltzmann,
## data,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [16857] {boltzmann,
## machin,
## model,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [16858] {boltzmann,
## data,
## model,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16859] {data,
## machin,
## model,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16860] {boltzmann,
## data,
## machin,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [16861] {boltzmann,
## data,
## featur,
## machin} => {show} 0.1000000 1.0000000 1.875000 3
## [16862] {boltzmann,
## featur,
## machin,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [16863] {boltzmann,
## data,
## featur,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16864] {data,
## featur,
## machin,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16865] {boltzmann,
## data,
## machin,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [16866] {boltzmann,
## data,
## featur,
## machin} => {model} 0.1000000 1.0000000 1.875000 3
## [16867] {boltzmann,
## featur,
## machin,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [16868] {boltzmann,
## data,
## featur,
## model} => {machin} 0.1000000 1.0000000 4.285714 3
## [16869] {data,
## featur,
## machin,
## model} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16870] {boltzmann,
## machin,
## model,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [16871] {boltzmann,
## featur,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16872] {boltzmann,
## featur,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [16873] {boltzmann,
## featur,
## model,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16874] {featur,
## machin,
## model,
## show} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16875] {boltzmann,
## data,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16876] {boltzmann,
## data,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16877] {boltzmann,
## model,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16878] {boltzmann,
## data,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16879] {data,
## model,
## show,
## task} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16880] {boltzmann,
## data,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16881] {boltzmann,
## data,
## featur,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16882] {boltzmann,
## featur,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16883] {boltzmann,
## data,
## featur,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16884] {boltzmann,
## data,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16885] {boltzmann,
## data,
## featur,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16886] {boltzmann,
## featur,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16887] {boltzmann,
## data,
## featur,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [16888] {boltzmann,
## model,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16889] {boltzmann,
## featur,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16890] {boltzmann,
## featur,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16891] {boltzmann,
## featur,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16892] {featur,
## model,
## show,
## task} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16893] {boltzmann,
## data,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [16894] {boltzmann,
## data,
## featur,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16895] {boltzmann,
## data,
## featur,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [16896] {boltzmann,
## featur,
## model,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [16897] {data,
## featur,
## model,
## show} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16898] {machin,
## restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16899] {machin,
## restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16900] {restrict,
## show,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16901] {machin,
## restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16902] {machin,
## show,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16903] {machin,
## restrict,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16904] {machin,
## model,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16905] {model,
## restrict,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16906] {machin,
## model,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16907] {machin,
## model,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16908] {machin,
## restrict,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16909] {machin,
## model,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16910] {model,
## restrict,
## show,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16911] {machin,
## model,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [16912] {machin,
## model,
## show,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [16913] {restrict,
## show,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16914] {model,
## restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16915] {model,
## restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16916] {model,
## restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16917] {model,
## show,
## algorithm,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [16918] {machin,
## restrict,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [16919] {machin,
## model,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [16920] {machin,
## model,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [16921] {model,
## restrict,
## show,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [16922] {machin,
## model,
## show,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16923] {data,
## machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16924] {machin,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16925] {data,
## machin,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16926] {data,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16927] {data,
## machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16928] {data,
## machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16929] {machin,
## model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16930] {data,
## machin,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16931] {data,
## model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16932] {data,
## machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16933] {data,
## machin,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16934] {featur,
## machin,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16935] {data,
## featur,
## machin,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16936] {data,
## featur,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16937] {data,
## featur,
## machin,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16938] {machin,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16939] {machin,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16940] {machin,
## model,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [16941] {model,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16942] {machin,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16943] {machin,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16944] {featur,
## machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16945] {featur,
## machin,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16946] {featur,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16947] {featur,
## machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16948] {machin,
## model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16949] {featur,
## machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16950] {featur,
## machin,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16951] {featur,
## model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16952] {featur,
## machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16953] {machin,
## restrict,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [16954] {machin,
## model,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [16955] {machin,
## model,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [16956] {model,
## restrict,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [16957] {machin,
## model,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16958] {data,
## machin,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16959] {data,
## machin,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16960] {machin,
## model,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [16961] {data,
## model,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16962] {data,
## machin,
## model,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16963] {data,
## machin,
## restrict,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [16964] {data,
## featur,
## machin,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16965] {featur,
## machin,
## restrict,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [16966] {data,
## featur,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16967] {data,
## featur,
## machin,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16968] {data,
## machin,
## model,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [16969] {data,
## featur,
## machin,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [16970] {featur,
## machin,
## model,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [16971] {data,
## featur,
## model,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [16972] {data,
## featur,
## machin,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16973] {machin,
## model,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [16974] {featur,
## machin,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16975] {featur,
## machin,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16976] {featur,
## model,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16977] {featur,
## machin,
## model,
## show} => {restrict} 0.1000000 0.7500000 5.625000 3
## [16978] {data,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16979] {data,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16980] {model,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16981] {data,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16982] {data,
## model,
## show,
## task} => {restrict} 0.1000000 0.7500000 5.625000 3
## [16983] {data,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16984] {data,
## featur,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16985] {featur,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16986] {data,
## featur,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16987] {data,
## model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16988] {data,
## featur,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16989] {featur,
## model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16990] {data,
## featur,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16991] {model,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16992] {featur,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16993] {featur,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16994] {featur,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16995] {featur,
## model,
## show,
## task} => {restrict} 0.1000000 0.7500000 5.625000 3
## [16996] {data,
## model,
## restrict,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [16997] {data,
## featur,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16998] {data,
## featur,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16999] {featur,
## model,
## restrict,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [17000] {data,
## featur,
## model,
## show} => {restrict} 0.1000000 0.7500000 5.625000 3
## [17001] {dataset,
## propos,
## learn,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [17002] {model,
## dataset,
## learn,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [17003] {model,
## dataset,
## propos,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [17004] {model,
## propos,
## learn,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17005] {approach,
## algorithm,
## neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [17006] {model,
## algorithm,
## neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [17007] {approach,
## model,
## neural,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [17008] {approach,
## model,
## algorithm,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [17009] {approach,
## model,
## algorithm,
## neural} => {order} 0.1000000 1.0000000 7.500000 3
## [17010] {approach,
## algorithm,
## neural,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [17011] {network,
## algorithm,
## neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [17012] {approach,
## network,
## neural,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [17013] {approach,
## network,
## algorithm,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [17014] {approach,
## network,
## algorithm,
## neural} => {order} 0.1000000 0.7500000 5.625000 3
## [17015] {model,
## algorithm,
## neural,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [17016] {network,
## algorithm,
## neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [17017] {model,
## network,
## neural,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [17018] {model,
## network,
## algorithm,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [17019] {model,
## network,
## algorithm,
## neural} => {order} 0.1000000 1.0000000 7.500000 3
## [17020] {approach,
## model,
## neural,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [17021] {approach,
## network,
## neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [17022] {model,
## network,
## neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [17023] {approach,
## model,
## network,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [17024] {approach,
## model,
## network,
## neural} => {order} 0.1000000 1.0000000 7.500000 3
## [17025] {approach,
## model,
## algorithm,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [17026] {approach,
## network,
## algorithm,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [17027] {model,
## network,
## algorithm,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [17028] {approach,
## model,
## network,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [17029] {approach,
## model,
## network,
## algorithm} => {order} 0.1000000 1.0000000 7.500000 3
## [17030] {model,
## show,
## algorithm,
## order} => {featur} 0.1000000 1.0000000 1.875000 3
## [17031] {featur,
## show,
## algorithm,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [17032] {featur,
## model,
## algorithm,
## order} => {show} 0.1000000 1.0000000 1.875000 3
## [17033] {featur,
## model,
## show,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [17034] {featur,
## model,
## show,
## algorithm} => {order} 0.1000000 0.7500000 5.625000 3
## [17035] {dataset,
## demonstr,
## learn,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [17036] {model,
## dataset,
## demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [17037] {model,
## demonstr,
## learn,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17038] {model,
## dataset,
## learn,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [17039] {model,
## dataset,
## demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [17040] {dataset,
## demonstr,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [17041] {featur,
## dataset,
## demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [17042] {featur,
## demonstr,
## learn,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17043] {featur,
## dataset,
## learn,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [17044] {featur,
## dataset,
## demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [17045] {model,
## dataset,
## demonstr,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [17046] {featur,
## dataset,
## demonstr,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [17047] {featur,
## model,
## demonstr,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17048] {featur,
## model,
## dataset,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [17049] {featur,
## model,
## dataset,
## demonstr} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [17050] {model,
## demonstr,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [17051] {featur,
## demonstr,
## learn,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [17052] {featur,
## model,
## demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [17053] {featur,
## model,
## learn,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [17054] {featur,
## model,
## demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [17055] {perform,
## problem,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [17056] {featur,
## perform,
## problem,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [17057] {featur,
## problem,
## learn,
## benchmark} => {perform} 0.1000000 1.0000000 2.142857 3
## [17058] {featur,
## perform,
## learn,
## benchmark} => {problem} 0.1000000 1.0000000 3.333333 3
## [17059] {featur,
## perform,
## problem,
## learn} => {benchmark} 0.1000000 0.7500000 5.625000 3
## [17060] {model,
## dataset,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [17061] {featur,
## dataset,
## learn,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [17062] {featur,
## model,
## dataset,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [17063] {featur,
## model,
## learn,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17064] {represent,
## propos,
## success,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [17065] {featur,
## represent,
## success,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [17066] {featur,
## propos,
## success,
## high} => {represent} 0.1000000 1.0000000 2.000000 3
## [17067] {featur,
## represent,
## propos,
## high} => {success} 0.1000000 1.0000000 3.750000 3
## [17068] {featur,
## represent,
## propos,
## success} => {high} 0.1000000 0.7500000 5.625000 3
## [17069] {classif,
## propos,
## learn,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [17070] {classif,
## featur,
## learn,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [17071] {classif,
## featur,
## propos,
## high} => {learn} 0.1000000 1.0000000 2.307692 3
## [17072] {featur,
## propos,
## learn,
## high} => {classif} 0.1000000 1.0000000 3.750000 3
## [17073] {classif,
## featur,
## propos,
## learn} => {high} 0.1000000 0.7500000 5.625000 3
## [17074] {predict,
## train,
## perform,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [17075] {predict,
## train,
## propos,
## challeng} => {perform} 0.1000000 1.0000000 2.142857 3
## [17076] {predict,
## perform,
## propos,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [17077] {predict,
## train,
## perform,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [17078] {train,
## perform,
## propos,
## challeng} => {predict} 0.1000000 1.0000000 7.500000 3
## [17079] {data,
## paper,
## predict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [17080] {model,
## paper,
## predict,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [17081] {data,
## model,
## paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [17082] {data,
## model,
## predict,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [17083] {data,
## model,
## paper,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [17084] {data,
## paper,
## predict,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [17085] {featur,
## paper,
## predict,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [17086] {data,
## featur,
## paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [17087] {data,
## featur,
## predict,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [17088] {data,
## featur,
## paper,
## train} => {predict} 0.1000000 0.7500000 5.625000 3
## [17089] {model,
## paper,
## predict,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [17090] {featur,
## paper,
## predict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [17091] {featur,
## model,
## paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [17092] {featur,
## model,
## predict,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [17093] {featur,
## model,
## paper,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [17094] {data,
## model,
## paper,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [17095] {data,
## featur,
## paper,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [17096] {featur,
## model,
## paper,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [17097] {data,
## featur,
## model,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [17098] {data,
## featur,
## model,
## paper} => {predict} 0.1000000 1.0000000 7.500000 3
## [17099] {data,
## model,
## predict,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [17100] {data,
## featur,
## predict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [17101] {featur,
## model,
## predict,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [17102] {data,
## featur,
## model,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [17103] {data,
## featur,
## model,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [17104] {train,
## recognit,
## challeng,
## face} => {represent} 0.1000000 1.0000000 2.000000 3
## [17105] {represent,
## recognit,
## challeng,
## face} => {train} 0.1000000 1.0000000 2.500000 3
## [17106] {represent,
## train,
## challeng,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [17107] {represent,
## train,
## recognit,
## face} => {challeng} 0.1000000 1.0000000 6.000000 3
## [17108] {represent,
## train,
## recognit,
## challeng} => {face} 0.1000000 1.0000000 7.500000 3
## [17109] {data,
## dataset,
## recognit,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [17110] {data,
## recognit,
## learn,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17111] {dataset,
## recognit,
## learn,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [17112] {data,
## dataset,
## learn,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [17113] {data,
## dataset,
## recognit,
## learn} => {face} 0.1000000 0.7500000 5.625000 3
## [17114] {data,
## dataset,
## recognit,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [17115] {data,
## featur,
## recognit,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17116] {featur,
## dataset,
## recognit,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [17117] {data,
## featur,
## dataset,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [17118] {data,
## featur,
## dataset,
## recognit} => {face} 0.1000000 1.0000000 7.500000 3
## [17119] {data,
## recognit,
## learn,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [17120] {data,
## featur,
## recognit,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [17121] {featur,
## recognit,
## learn,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [17122] {data,
## featur,
## learn,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [17123] {data,
## featur,
## recognit,
## learn} => {face} 0.1000000 0.7500000 5.625000 3
## [17124] {dataset,
## recognit,
## learn,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [17125] {featur,
## dataset,
## recognit,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [17126] {featur,
## recognit,
## learn,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17127] {featur,
## dataset,
## learn,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [17128] {featur,
## dataset,
## recognit,
## learn} => {face} 0.1000000 1.0000000 7.500000 3
## [17129] {data,
## dataset,
## learn,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [17130] {data,
## featur,
## dataset,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [17131] {data,
## featur,
## learn,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17132] {featur,
## dataset,
## learn,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [17133] {approach,
## method,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17134] {approach,
## method,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17135] {method,
## dataset,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17136] {approach,
## dataset,
## learn,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [17137] {approach,
## method,
## dataset,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17138] {approach,
## method,
## dataset,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17139] {approach,
## method,
## show,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17140] {method,
## show,
## dataset,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17141] {approach,
## show,
## dataset,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [17142] {approach,
## method,
## dataset,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [17143] {approach,
## method,
## model,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17144] {method,
## model,
## dataset,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17145] {approach,
## model,
## dataset,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [17146] {approach,
## method,
## model,
## dataset} => {sourc} 0.1000000 0.7500000 5.625000 3
## [17147] {approach,
## method,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17148] {approach,
## method,
## show,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17149] {method,
## show,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17150] {approach,
## show,
## learn,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [17151] {approach,
## method,
## show,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17152] {approach,
## method,
## learn,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [17153] {approach,
## method,
## model,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17154] {method,
## model,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17155] {approach,
## model,
## learn,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [17156] {approach,
## method,
## model,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17157] {approach,
## method,
## show,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [17158] {approach,
## method,
## model,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17159] {method,
## model,
## show,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17160] {approach,
## model,
## show,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [17161] {method,
## dataset,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17162] {method,
## show,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17163] {method,
## show,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17164] {show,
## dataset,
## learn,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [17165] {method,
## show,
## dataset,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17166] {method,
## dataset,
## learn,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [17167] {method,
## model,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17168] {method,
## model,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17169] {model,
## dataset,
## learn,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [17170] {method,
## model,
## dataset,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17171] {method,
## show,
## dataset,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [17172] {method,
## model,
## dataset,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17173] {method,
## model,
## show,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17174] {model,
## show,
## dataset,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [17175] {method,
## model,
## show,
## dataset} => {sourc} 0.1000000 0.7500000 5.625000 3
## [17176] {method,
## show,
## learn,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [17177] {method,
## model,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17178] {method,
## model,
## show,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17179] {model,
## show,
## learn,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [17180] {method,
## model,
## show,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17181] {approach,
## dataset,
## learn,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [17182] {approach,
## represent,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17183] {approach,
## represent,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17184] {represent,
## dataset,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17185] {approach,
## represent,
## dataset,
## learn} => {sourc} 0.1000000 0.7500000 5.625000 3
## [17186] {approach,
## dataset,
## learn,
## sourc} => {show} 0.1333333 1.0000000 1.875000 4
## [17187] {approach,
## show,
## dataset,
## sourc} => {learn} 0.1333333 1.0000000 2.307692 4
## [17188] {approach,
## show,
## learn,
## sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [17189] {show,
## dataset,
## learn,
## sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [17190] {approach,
## show,
## dataset,
## learn} => {sourc} 0.1333333 1.0000000 7.500000 4
## [17191] {approach,
## dataset,
## learn,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [17192] {approach,
## dataset,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17193] {approach,
## propos,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17194] {dataset,
## propos,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17195] {approach,
## dataset,
## propos,
## learn} => {sourc} 0.1000000 0.7500000 5.625000 3
## [17196] {approach,
## dataset,
## learn,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [17197] {approach,
## model,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17198] {approach,
## model,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17199] {model,
## dataset,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17200] {approach,
## model,
## dataset,
## learn} => {sourc} 0.1000000 0.7500000 5.625000 3
## [17201] {approach,
## represent,
## dataset,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17202] {approach,
## show,
## dataset,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [17203] {approach,
## represent,
## show,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17204] {represent,
## show,
## dataset,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17205] {approach,
## represent,
## show,
## dataset} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17206] {approach,
## represent,
## dataset,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [17207] {approach,
## dataset,
## propos,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [17208] {approach,
## represent,
## propos,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17209] {represent,
## dataset,
## propos,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17210] {approach,
## represent,
## dataset,
## propos} => {sourc} 0.1000000 0.7500000 5.625000 3
## [17211] {approach,
## show,
## dataset,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [17212] {approach,
## dataset,
## propos,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17213] {approach,
## show,
## propos,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17214] {show,
## dataset,
## propos,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17215] {approach,
## show,
## dataset,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [17216] {approach,
## model,
## dataset,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17217] {approach,
## model,
## show,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17218] {model,
## show,
## dataset,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17219] {approach,
## model,
## show,
## dataset} => {sourc} 0.1000000 0.7500000 5.625000 3
## [17220] {approach,
## represent,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17221] {approach,
## show,
## learn,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [17222] {approach,
## represent,
## show,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17223] {represent,
## show,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17224] {approach,
## represent,
## show,
## learn} => {sourc} 0.1000000 0.7500000 5.625000 3
## [17225] {approach,
## represent,
## learn,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [17226] {approach,
## propos,
## learn,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [17227] {approach,
## represent,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17228] {represent,
## propos,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17229] {approach,
## show,
## learn,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [17230] {approach,
## propos,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17231] {approach,
## show,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17232] {show,
## propos,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17233] {approach,
## show,
## propos,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17234] {approach,
## show,
## learn,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [17235] {approach,
## model,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17236] {approach,
## model,
## show,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17237] {model,
## show,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17238] {approach,
## model,
## show,
## learn} => {sourc} 0.1000000 0.7500000 5.625000 3
## [17239] {approach,
## represent,
## show,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [17240] {approach,
## represent,
## propos,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17241] {approach,
## show,
## propos,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [17242] {represent,
## show,
## propos,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [17243] {approach,
## represent,
## show,
## propos} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17244] {represent,
## dataset,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17245] {show,
## dataset,
## learn,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [17246] {represent,
## show,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17247] {represent,
## show,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17248] {represent,
## show,
## dataset,
## learn} => {sourc} 0.1000000 0.7500000 5.625000 3
## [17249] {represent,
## dataset,
## learn,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [17250] {dataset,
## propos,
## learn,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [17251] {represent,
## dataset,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17252] {represent,
## propos,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17253] {show,
## dataset,
## learn,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [17254] {dataset,
## propos,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17255] {show,
## dataset,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17256] {show,
## propos,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17257] {show,
## dataset,
## propos,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17258] {show,
## dataset,
## learn,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [17259] {model,
## dataset,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17260] {model,
## show,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17261] {model,
## show,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17262] {model,
## show,
## dataset,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17263] {represent,
## show,
## dataset,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [17264] {represent,
## dataset,
## propos,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17265] {show,
## dataset,
## propos,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [17266] {represent,
## show,
## propos,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17267] {represent,
## show,
## dataset,
## propos} => {sourc} 0.1000000 1.0000000 7.500000 3
## [17268] {represent,
## show,
## learn,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [17269] {represent,
## propos,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [17270] {show,
## propos,
## learn,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [17271] {represent,
## show,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17272] {represent,
## show,
## propos,
## learn} => {sourc} 0.1000000 0.7500000 5.625000 3
## [17273] {represent,
## general,
## recognit,
## variat} => {show} 0.1000000 1.0000000 1.875000 3
## [17274] {show,
## general,
## recognit,
## variat} => {represent} 0.1000000 1.0000000 2.000000 3
## [17275] {represent,
## show,
## general,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [17276] {represent,
## show,
## recognit,
## variat} => {general} 0.1000000 1.0000000 5.000000 3
## [17277] {represent,
## show,
## general,
## recognit} => {variat} 0.1000000 1.0000000 7.500000 3
## [17278] {data,
## task,
## recognit,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [17279] {task,
## recognit,
## learn,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [17280] {data,
## recognit,
## learn,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [17281] {data,
## task,
## learn,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [17282] {data,
## task,
## recognit,
## learn} => {variat} 0.1000000 0.7500000 5.625000 3
## [17283] {data,
## task,
## recognit,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [17284] {featur,
## task,
## recognit,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [17285] {data,
## featur,
## recognit,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [17286] {data,
## featur,
## task,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [17287] {data,
## featur,
## task,
## recognit} => {variat} 0.1000000 1.0000000 7.500000 3
## [17288] {task,
## recognit,
## learn,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [17289] {featur,
## task,
## recognit,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [17290] {featur,
## recognit,
## learn,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [17291] {featur,
## task,
## learn,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [17292] {featur,
## task,
## recognit,
## learn} => {variat} 0.1000000 1.0000000 7.500000 3
## [17293] {data,
## recognit,
## learn,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [17294] {data,
## featur,
## recognit,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [17295] {featur,
## recognit,
## learn,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [17296] {data,
## featur,
## learn,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [17297] {data,
## featur,
## recognit,
## learn} => {variat} 0.1000000 0.7500000 5.625000 3
## [17298] {data,
## task,
## learn,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [17299] {data,
## featur,
## task,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [17300] {featur,
## task,
## learn,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [17301] {data,
## featur,
## learn,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [17302] {model,
## process,
## introduc,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [17303] {featur,
## process,
## introduc,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [17304] {featur,
## model,
## process,
## introduc} => {learn} 0.1000000 1.0000000 2.307692 3
## [17305] {featur,
## model,
## introduc,
## learn} => {process} 0.1000000 1.0000000 5.000000 3
## [17306] {featur,
## model,
## process,
## learn} => {introduc} 0.1000000 1.0000000 7.500000 3
## [17307] {approach,
## data,
## import,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17308] {approach,
## import,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [17309] {data,
## import,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [17310] {approach,
## data,
## import,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [17311] {approach,
## data,
## import,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [17312] {approach,
## import,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [17313] {data,
## import,
## model,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [17314] {approach,
## data,
## import,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [17315] {approach,
## data,
## model,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [17316] {approach,
## data,
## import,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17317] {approach,
## featur,
## import,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [17318] {data,
## featur,
## import,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [17319] {approach,
## data,
## featur,
## import} => {task} 0.1000000 1.0000000 2.727273 3
## [17320] {approach,
## data,
## featur,
## task} => {import} 0.1000000 0.7500000 5.625000 3
## [17321] {approach,
## import,
## represent,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [17322] {approach,
## import,
## model,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17323] {import,
## model,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [17324] {approach,
## import,
## model,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [17325] {approach,
## model,
## represent,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [17326] {approach,
## import,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17327] {approach,
## featur,
## import,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17328] {featur,
## import,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [17329] {approach,
## featur,
## import,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [17330] {approach,
## import,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17331] {approach,
## featur,
## import,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [17332] {featur,
## import,
## model,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [17333] {approach,
## featur,
## import,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [17334] {approach,
## featur,
## model,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [17335] {data,
## import,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [17336] {data,
## import,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [17337] {import,
## represent,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [17338] {data,
## import,
## represent,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [17339] {data,
## import,
## task,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [17340] {data,
## import,
## model,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [17341] {import,
## model,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [17342] {data,
## import,
## model,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [17343] {data,
## import,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [17344] {data,
## featur,
## import,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [17345] {featur,
## import,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [17346] {data,
## featur,
## import,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [17347] {data,
## import,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [17348] {data,
## import,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17349] {import,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [17350] {data,
## import,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [17351] {data,
## import,
## represent,
## task} => {model} 0.1333333 1.0000000 1.875000 4
## [17352] {data,
## import,
## model,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [17353] {import,
## model,
## represent,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [17354] {data,
## import,
## model,
## represent} => {task} 0.1333333 1.0000000 2.727273 4
## [17355] {data,
## model,
## represent,
## task} => {import} 0.1333333 1.0000000 7.500000 4
## [17356] {data,
## import,
## represent,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [17357] {data,
## featur,
## import,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [17358] {featur,
## import,
## represent,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [17359] {data,
## featur,
## import,
## represent} => {task} 0.1333333 1.0000000 2.727273 4
## [17360] {data,
## import,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [17361] {data,
## import,
## model,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [17362] {import,
## model,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [17363] {data,
## import,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [17364] {data,
## model,
## show,
## task} => {import} 0.1000000 0.7500000 5.625000 3
## [17365] {data,
## import,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17366] {data,
## featur,
## import,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [17367] {featur,
## import,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [17368] {data,
## featur,
## import,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [17369] {data,
## import,
## model,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [17370] {data,
## featur,
## import,
## task} => {model} 0.1333333 1.0000000 1.875000 4
## [17371] {featur,
## import,
## model,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [17372] {data,
## featur,
## import,
## model} => {task} 0.1333333 1.0000000 2.727273 4
## [17373] {import,
## represent,
## task,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [17374] {import,
## model,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [17375] {import,
## model,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [17376] {import,
## model,
## represent,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [17377] {model,
## represent,
## task,
## learn} => {import} 0.1000000 1.0000000 7.500000 3
## [17378] {import,
## represent,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [17379] {featur,
## import,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [17380] {featur,
## import,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [17381] {featur,
## import,
## represent,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [17382] {import,
## model,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [17383] {featur,
## import,
## task,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [17384] {featur,
## import,
## model,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [17385] {featur,
## import,
## model,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [17386] {import,
## represent,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [17387] {import,
## model,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [17388] {import,
## model,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17389] {import,
## model,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [17390] {model,
## represent,
## show,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [17391] {import,
## represent,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17392] {featur,
## import,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [17393] {featur,
## import,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17394] {featur,
## import,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [17395] {featur,
## represent,
## show,
## task} => {import} 0.1000000 0.7500000 5.625000 3
## [17396] {import,
## model,
## represent,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [17397] {featur,
## import,
## represent,
## task} => {model} 0.1333333 1.0000000 1.875000 4
## [17398] {featur,
## import,
## model,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [17399] {featur,
## import,
## model,
## represent} => {task} 0.1333333 1.0000000 2.727273 4
## [17400] {featur,
## model,
## represent,
## task} => {import} 0.1333333 1.0000000 7.500000 4
## [17401] {import,
## model,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17402] {featur,
## import,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [17403] {featur,
## import,
## model,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [17404] {featur,
## import,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [17405] {featur,
## model,
## show,
## task} => {import} 0.1000000 0.7500000 5.625000 3
## [17406] {approach,
## data,
## import,
## represent} => {model} 0.1000000 1.0000000 1.875000 3
## [17407] {approach,
## data,
## import,
## model} => {represent} 0.1000000 1.0000000 2.000000 3
## [17408] {approach,
## import,
## model,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [17409] {data,
## import,
## model,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [17410] {approach,
## data,
## model,
## represent} => {import} 0.1000000 1.0000000 7.500000 3
## [17411] {approach,
## data,
## import,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [17412] {approach,
## data,
## featur,
## import} => {represent} 0.1000000 1.0000000 2.000000 3
## [17413] {approach,
## featur,
## import,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [17414] {data,
## featur,
## import,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [17415] {approach,
## data,
## featur,
## represent} => {import} 0.1000000 0.7500000 5.625000 3
## [17416] {approach,
## data,
## import,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [17417] {approach,
## data,
## featur,
## import} => {model} 0.1000000 1.0000000 1.875000 3
## [17418] {approach,
## featur,
## import,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [17419] {data,
## featur,
## import,
## model} => {approach} 0.1000000 0.7500000 1.875000 3
## [17420] {approach,
## data,
## featur,
## model} => {import} 0.1000000 1.0000000 7.500000 3
## [17421] {approach,
## import,
## model,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [17422] {approach,
## featur,
## import,
## represent} => {model} 0.1000000 1.0000000 1.875000 3
## [17423] {approach,
## featur,
## import,
## model} => {represent} 0.1000000 1.0000000 2.000000 3
## [17424] {featur,
## import,
## model,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [17425] {approach,
## featur,
## model,
## represent} => {import} 0.1000000 0.7500000 5.625000 3
## [17426] {data,
## import,
## represent,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [17427] {data,
## import,
## model,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [17428] {data,
## import,
## model,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [17429] {import,
## model,
## represent,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [17430] {data,
## model,
## represent,
## learn} => {import} 0.1000000 0.7500000 5.625000 3
## [17431] {data,
## import,
## represent,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [17432] {data,
## featur,
## import,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [17433] {data,
## featur,
## import,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [17434] {featur,
## import,
## represent,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [17435] {data,
## import,
## model,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [17436] {data,
## featur,
## import,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [17437] {data,
## featur,
## import,
## model} => {learn} 0.1000000 0.7500000 1.730769 3
## [17438] {featur,
## import,
## model,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [17439] {data,
## import,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [17440] {data,
## import,
## model,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [17441] {data,
## import,
## model,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [17442] {import,
## model,
## represent,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [17443] {data,
## model,
## represent,
## show} => {import} 0.1000000 1.0000000 7.500000 3
## [17444] {data,
## import,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [17445] {data,
## featur,
## import,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [17446] {data,
## featur,
## import,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [17447] {featur,
## import,
## represent,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [17448] {data,
## featur,
## represent,
## show} => {import} 0.1000000 0.7500000 5.625000 3
## [17449] {data,
## import,
## model,
## represent} => {featur} 0.1333333 1.0000000 1.875000 4
## [17450] {data,
## featur,
## import,
## represent} => {model} 0.1333333 1.0000000 1.875000 4
## [17451] {data,
## featur,
## import,
## model} => {represent} 0.1333333 1.0000000 2.000000 4
## [17452] {featur,
## import,
## model,
## represent} => {data} 0.1333333 1.0000000 2.307692 4
## [17453] {data,
## featur,
## model,
## represent} => {import} 0.1333333 0.8000000 6.000000 4
## [17454] {data,
## import,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [17455] {data,
## featur,
## import,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [17456] {data,
## featur,
## import,
## model} => {show} 0.1000000 0.7500000 1.406250 3
## [17457] {featur,
## import,
## model,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [17458] {data,
## featur,
## model,
## show} => {import} 0.1000000 0.7500000 5.625000 3
## [17459] {import,
## model,
## represent,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [17460] {featur,
## import,
## represent,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [17461] {featur,
## import,
## model,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [17462] {featur,
## import,
## model,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [17463] {import,
## model,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [17464] {featur,
## import,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [17465] {featur,
## import,
## model,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [17466] {featur,
## import,
## model,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [17467] {featur,
## model,
## represent,
## show} => {import} 0.1000000 0.7500000 5.625000 3
## [17468] {perform,
## problem,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17469] {problem,
## learn,
## art,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [17470] {perform,
## learn,
## art,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [17471] {perform,
## problem,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17472] {perform,
## problem,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17473] {perform,
## problem,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17474] {propos,
## problem,
## art,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [17475] {perform,
## propos,
## art,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [17476] {perform,
## propos,
## problem,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17477] {perform,
## propos,
## problem,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17478] {perform,
## problem,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17479] {model,
## problem,
## art,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [17480] {model,
## perform,
## art,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [17481] {model,
## perform,
## problem,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17482] {model,
## perform,
## problem,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17483] {problem,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17484] {propos,
## problem,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17485] {propos,
## learn,
## art,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [17486] {propos,
## problem,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17487] {propos,
## problem,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17488] {problem,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17489] {model,
## problem,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17490] {model,
## learn,
## art,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [17491] {model,
## problem,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17492] {model,
## problem,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17493] {propos,
## problem,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17494] {model,
## problem,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17495] {model,
## propos,
## art,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [17496] {model,
## propos,
## problem,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17497] {model,
## propos,
## problem,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17498] {data,
## task,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17499] {task,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17500] {data,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17501] {data,
## task,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17502] {data,
## task,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17503] {data,
## task,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17504] {task,
## propos,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17505] {data,
## propos,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17506] {data,
## task,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17507] {data,
## task,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17508] {data,
## task,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17509] {model,
## task,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17510] {data,
## model,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17511] {data,
## model,
## task,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17512] {data,
## model,
## task,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17513] {data,
## task,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17514] {featur,
## task,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17515] {data,
## featur,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17516] {data,
## featur,
## task,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17517] {data,
## featur,
## task,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17518] {task,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17519] {task,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17520] {propos,
## learn,
## art,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [17521] {task,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17522] {task,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17523] {task,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17524] {model,
## task,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17525] {model,
## learn,
## art,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [17526] {model,
## task,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17527] {model,
## task,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17528] {task,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17529] {featur,
## task,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17530] {featur,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17531] {featur,
## task,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17532] {featur,
## task,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17533] {task,
## propos,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17534] {model,
## task,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17535] {model,
## propos,
## art,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [17536] {model,
## task,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17537] {model,
## task,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17538] {task,
## propos,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17539] {featur,
## task,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17540] {featur,
## propos,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17541] {featur,
## task,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17542] {featur,
## task,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17543] {model,
## task,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17544] {featur,
## task,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17545] {featur,
## model,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17546] {featur,
## model,
## task,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17547] {featur,
## model,
## task,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17548] {perform,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17549] {perform,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17550] {propos,
## learn,
## art,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [17551] {perform,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17552] {perform,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17553] {perform,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17554] {model,
## perform,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17555] {model,
## learn,
## art,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [17556] {model,
## perform,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17557] {model,
## perform,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17558] {perform,
## propos,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17559] {model,
## perform,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17560] {model,
## propos,
## art,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [17561] {model,
## perform,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17562] {model,
## perform,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17563] {data,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17564] {data,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17565] {propos,
## learn,
## art,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [17566] {data,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17567] {data,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17568] {data,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17569] {data,
## model,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17570] {model,
## learn,
## art,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [17571] {data,
## model,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17572] {data,
## model,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17573] {data,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17574] {data,
## featur,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17575] {featur,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17576] {data,
## featur,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17577] {data,
## featur,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17578] {data,
## propos,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17579] {data,
## model,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17580] {model,
## propos,
## art,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [17581] {data,
## model,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17582] {data,
## model,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17583] {data,
## propos,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17584] {data,
## featur,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17585] {featur,
## propos,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17586] {data,
## featur,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17587] {data,
## featur,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17588] {data,
## model,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17589] {data,
## featur,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17590] {featur,
## model,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17591] {data,
## featur,
## model,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17592] {data,
## featur,
## model,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17593] {dataset,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17594] {dataset,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17595] {propos,
## learn,
## art,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [17596] {dataset,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17597] {dataset,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17598] {dataset,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17599] {model,
## dataset,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17600] {model,
## learn,
## art,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [17601] {model,
## dataset,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17602] {model,
## dataset,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17603] {dataset,
## propos,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17604] {model,
## dataset,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17605] {model,
## propos,
## art,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [17606] {model,
## dataset,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17607] {model,
## dataset,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17608] {propos,
## learn,
## art,
## state} => {model} 0.1333333 1.0000000 1.875000 4
## [17609] {model,
## learn,
## art,
## state} => {propos} 0.1333333 1.0000000 2.000000 4
## [17610] {model,
## propos,
## art,
## state} => {learn} 0.1333333 1.0000000 2.307692 4
## [17611] {model,
## propos,
## learn,
## art} => {state} 0.1333333 1.0000000 7.500000 4
## [17612] {model,
## propos,
## learn,
## state} => {art} 0.1333333 1.0000000 7.500000 4
## [17613] {propos,
## learn,
## art,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [17614] {featur,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17615] {featur,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17616] {featur,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17617] {featur,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17618] {model,
## learn,
## art,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [17619] {featur,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17620] {featur,
## model,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17621] {featur,
## model,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17622] {featur,
## model,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17623] {model,
## propos,
## art,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [17624] {featur,
## propos,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17625] {featur,
## model,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17626] {featur,
## model,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [17627] {featur,
## model,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [17628] {perform,
## problem,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17629] {perform,
## propos,
## problem,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17630] {propos,
## problem,
## learn,
## art} => {perform} 0.1000000 1.0000000 2.142857 3
## [17631] {perform,
## propos,
## learn,
## art} => {problem} 0.1000000 1.0000000 3.333333 3
## [17632] {perform,
## propos,
## problem,
## learn} => {art} 0.1000000 0.7500000 5.625000 3
## [17633] {perform,
## problem,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17634] {model,
## perform,
## problem,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17635] {model,
## problem,
## learn,
## art} => {perform} 0.1000000 1.0000000 2.142857 3
## [17636] {model,
## perform,
## learn,
## art} => {problem} 0.1000000 1.0000000 3.333333 3
## [17637] {model,
## perform,
## problem,
## learn} => {art} 0.1000000 0.7500000 5.625000 3
## [17638] {perform,
## propos,
## problem,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17639] {model,
## perform,
## problem,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17640] {model,
## propos,
## problem,
## art} => {perform} 0.1000000 1.0000000 2.142857 3
## [17641] {model,
## perform,
## propos,
## art} => {problem} 0.1000000 1.0000000 3.333333 3
## [17642] {model,
## perform,
## propos,
## problem} => {art} 0.1000000 1.0000000 7.500000 3
## [17643] {propos,
## problem,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17644] {model,
## problem,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17645] {model,
## propos,
## problem,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17646] {model,
## propos,
## learn,
## art} => {problem} 0.1000000 0.7500000 2.500000 3
## [17647] {model,
## propos,
## problem,
## learn} => {art} 0.1000000 1.0000000 7.500000 3
## [17648] {data,
## task,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17649] {data,
## task,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17650] {task,
## propos,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [17651] {data,
## propos,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [17652] {data,
## task,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17653] {data,
## model,
## task,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17654] {model,
## task,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [17655] {data,
## model,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [17656] {data,
## task,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [17657] {data,
## featur,
## task,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17658] {featur,
## task,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [17659] {data,
## featur,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [17660] {data,
## task,
## propos,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17661] {data,
## model,
## task,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17662] {model,
## task,
## propos,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [17663] {data,
## model,
## propos,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [17664] {data,
## model,
## task,
## propos} => {art} 0.1000000 0.7500000 5.625000 3
## [17665] {data,
## task,
## propos,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [17666] {data,
## featur,
## task,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17667] {featur,
## task,
## propos,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [17668] {data,
## featur,
## propos,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [17669] {data,
## model,
## task,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [17670] {data,
## featur,
## task,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17671] {featur,
## model,
## task,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [17672] {data,
## featur,
## model,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [17673] {task,
## propos,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17674] {model,
## task,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17675] {model,
## task,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17676] {model,
## propos,
## learn,
## art} => {task} 0.1000000 0.7500000 2.045455 3
## [17677] {model,
## task,
## propos,
## learn} => {art} 0.1000000 0.7500000 5.625000 3
## [17678] {task,
## propos,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [17679] {featur,
## task,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17680] {featur,
## task,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17681] {featur,
## propos,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [17682] {model,
## task,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [17683] {featur,
## task,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17684] {featur,
## model,
## task,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17685] {featur,
## model,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [17686] {model,
## task,
## propos,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [17687] {featur,
## task,
## propos,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17688] {featur,
## model,
## task,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17689] {featur,
## model,
## propos,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [17690] {featur,
## model,
## task,
## propos} => {art} 0.1000000 0.7500000 5.625000 3
## [17691] {perform,
## propos,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17692] {model,
## perform,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17693] {model,
## perform,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17694] {model,
## propos,
## learn,
## art} => {perform} 0.1000000 0.7500000 1.607143 3
## [17695] {model,
## perform,
## propos,
## learn} => {art} 0.1000000 0.7500000 5.625000 3
## [17696] {data,
## propos,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17697] {data,
## model,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17698] {data,
## model,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17699] {model,
## propos,
## learn,
## art} => {data} 0.1000000 0.7500000 1.730769 3
## [17700] {data,
## propos,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [17701] {data,
## featur,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17702] {data,
## featur,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17703] {featur,
## propos,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [17704] {data,
## model,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [17705] {data,
## featur,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17706] {data,
## featur,
## model,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17707] {featur,
## model,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [17708] {data,
## model,
## propos,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [17709] {data,
## featur,
## propos,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17710] {data,
## featur,
## model,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17711] {featur,
## model,
## propos,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [17712] {dataset,
## propos,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17713] {model,
## dataset,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17714] {model,
## dataset,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17715] {model,
## propos,
## learn,
## art} => {dataset} 0.1000000 0.7500000 1.730769 3
## [17716] {model,
## propos,
## learn,
## art} => {featur} 0.1000000 0.7500000 1.406250 3
## [17717] {featur,
## propos,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [17718] {featur,
## model,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [17719] {featur,
## model,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [17720] {perform,
## problem,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17721] {perform,
## propos,
## problem,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17722] {propos,
## problem,
## learn,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [17723] {perform,
## propos,
## learn,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [17724] {perform,
## propos,
## problem,
## learn} => {state} 0.1000000 0.7500000 5.625000 3
## [17725] {perform,
## problem,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17726] {model,
## perform,
## problem,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17727] {model,
## problem,
## learn,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [17728] {model,
## perform,
## learn,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [17729] {model,
## perform,
## problem,
## learn} => {state} 0.1000000 0.7500000 5.625000 3
## [17730] {perform,
## propos,
## problem,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17731] {model,
## perform,
## problem,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17732] {model,
## propos,
## problem,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [17733] {model,
## perform,
## propos,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [17734] {model,
## perform,
## propos,
## problem} => {state} 0.1000000 1.0000000 7.500000 3
## [17735] {propos,
## problem,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17736] {model,
## problem,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17737] {model,
## propos,
## problem,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17738] {model,
## propos,
## learn,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [17739] {model,
## propos,
## problem,
## learn} => {state} 0.1000000 1.0000000 7.500000 3
## [17740] {data,
## task,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17741] {data,
## task,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17742] {task,
## propos,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17743] {data,
## propos,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17744] {data,
## task,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17745] {data,
## model,
## task,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17746] {model,
## task,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17747] {data,
## model,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17748] {data,
## task,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17749] {data,
## featur,
## task,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17750] {featur,
## task,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17751] {data,
## featur,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17752] {data,
## task,
## propos,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17753] {data,
## model,
## task,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17754] {model,
## task,
## propos,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17755] {data,
## model,
## propos,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17756] {data,
## model,
## task,
## propos} => {state} 0.1000000 0.7500000 5.625000 3
## [17757] {data,
## task,
## propos,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17758] {data,
## featur,
## task,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17759] {featur,
## task,
## propos,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17760] {data,
## featur,
## propos,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17761] {data,
## model,
## task,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17762] {data,
## featur,
## task,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17763] {featur,
## model,
## task,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17764] {data,
## featur,
## model,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17765] {task,
## propos,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17766] {model,
## task,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17767] {model,
## task,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17768] {model,
## propos,
## learn,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [17769] {model,
## task,
## propos,
## learn} => {state} 0.1000000 0.7500000 5.625000 3
## [17770] {task,
## propos,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17771] {featur,
## task,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17772] {featur,
## task,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17773] {featur,
## propos,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17774] {model,
## task,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17775] {featur,
## task,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17776] {featur,
## model,
## task,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17777] {featur,
## model,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17778] {model,
## task,
## propos,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17779] {featur,
## task,
## propos,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17780] {featur,
## model,
## task,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17781] {featur,
## model,
## propos,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [17782] {featur,
## model,
## task,
## propos} => {state} 0.1000000 0.7500000 5.625000 3
## [17783] {perform,
## propos,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17784] {model,
## perform,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17785] {model,
## perform,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17786] {model,
## propos,
## learn,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [17787] {model,
## perform,
## propos,
## learn} => {state} 0.1000000 0.7500000 5.625000 3
## [17788] {data,
## propos,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17789] {data,
## model,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17790] {data,
## model,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17791] {model,
## propos,
## learn,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [17792] {data,
## propos,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17793] {data,
## featur,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17794] {data,
## featur,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17795] {featur,
## propos,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17796] {data,
## model,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17797] {data,
## featur,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17798] {data,
## featur,
## model,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17799] {featur,
## model,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17800] {data,
## model,
## propos,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [17801] {data,
## featur,
## propos,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17802] {data,
## featur,
## model,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17803] {featur,
## model,
## propos,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [17804] {dataset,
## propos,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17805] {model,
## dataset,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17806] {model,
## dataset,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17807] {model,
## propos,
## learn,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [17808] {model,
## propos,
## learn,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [17809] {featur,
## propos,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [17810] {featur,
## model,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [17811] {featur,
## model,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [17812] {show,
## general,
## joint,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [17813] {general,
## joint,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [17814] {show,
## general,
## joint,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [17815] {show,
## joint,
## perform,
## propos} => {general} 0.1000000 1.0000000 5.000000 3
## [17816] {show,
## general,
## perform,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [17817] {approach,
## method,
## dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [17818] {approach,
## method,
## show,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17819] {method,
## show,
## dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [17820] {approach,
## show,
## dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [17821] {approach,
## method,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [17822] {approach,
## method,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17823] {method,
## dataset,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [17824] {approach,
## dataset,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [17825] {approach,
## method,
## dataset,
## propos} => {joint} 0.1000000 0.7500000 5.625000 3
## [17826] {approach,
## method,
## dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [17827] {approach,
## method,
## model,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17828] {method,
## model,
## dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [17829] {approach,
## model,
## dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [17830] {approach,
## method,
## model,
## dataset} => {joint} 0.1000000 0.7500000 5.625000 3
## [17831] {approach,
## method,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [17832] {approach,
## method,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [17833] {method,
## show,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [17834] {approach,
## show,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [17835] {approach,
## method,
## show,
## propos} => {joint} 0.1000000 0.7500000 5.625000 3
## [17836] {approach,
## method,
## show,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [17837] {approach,
## method,
## model,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [17838] {method,
## model,
## show,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [17839] {approach,
## model,
## show,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [17840] {approach,
## method,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [17841] {approach,
## method,
## model,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [17842] {method,
## model,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [17843] {approach,
## model,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [17844] {approach,
## method,
## model,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [17845] {method,
## show,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [17846] {method,
## dataset,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [17847] {method,
## show,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17848] {show,
## dataset,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [17849] {method,
## show,
## dataset,
## propos} => {joint} 0.1000000 0.7500000 5.625000 3
## [17850] {method,
## show,
## dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [17851] {method,
## model,
## dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [17852] {method,
## model,
## show,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17853] {model,
## show,
## dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [17854] {method,
## model,
## show,
## dataset} => {joint} 0.1000000 0.7500000 5.625000 3
## [17855] {method,
## dataset,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [17856] {method,
## model,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [17857] {method,
## model,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17858] {model,
## dataset,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [17859] {method,
## model,
## dataset,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [17860] {method,
## show,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [17861] {method,
## model,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [17862] {method,
## model,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [17863] {model,
## show,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [17864] {method,
## model,
## show,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [17865] {approach,
## show,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [17866] {approach,
## dataset,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [17867] {approach,
## show,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17868] {show,
## dataset,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [17869] {approach,
## show,
## dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [17870] {approach,
## model,
## dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [17871] {approach,
## model,
## show,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17872] {model,
## show,
## dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [17873] {approach,
## model,
## show,
## dataset} => {joint} 0.1000000 0.7500000 5.625000 3
## [17874] {approach,
## dataset,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [17875] {approach,
## model,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [17876] {approach,
## model,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17877] {model,
## dataset,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [17878] {approach,
## show,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [17879] {approach,
## model,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [17880] {approach,
## model,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [17881] {model,
## show,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [17882] {approach,
## model,
## show,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [17883] {show,
## dataset,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [17884] {model,
## show,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [17885] {model,
## dataset,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [17886] {model,
## show,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [17887] {model,
## show,
## dataset,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [17888] {classif,
## addit,
## imag,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [17889] {propos,
## addit,
## imag,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [17890] {classif,
## propos,
## addit,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [17891] {classif,
## propos,
## imag,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [17892] {classif,
## propos,
## addit,
## imag} => {studi} 0.1000000 1.0000000 7.500000 3
## [17893] {approach,
## evalu,
## method,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17894] {evalu,
## method,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [17895] {approach,
## evalu,
## method,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [17896] {approach,
## evalu,
## represent,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [17897] {approach,
## method,
## represent,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [17898] {approach,
## evalu,
## method,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17899] {evalu,
## featur,
## method,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [17900] {approach,
## evalu,
## featur,
## method} => {task} 0.1000000 1.0000000 2.727273 3
## [17901] {approach,
## evalu,
## featur,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [17902] {approach,
## featur,
## method,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [17903] {approach,
## evalu,
## method,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [17904] {evalu,
## method,
## network,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [17905] {approach,
## evalu,
## method,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [17906] {approach,
## evalu,
## network,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [17907] {approach,
## method,
## network,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [17908] {evalu,
## method,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17909] {evalu,
## featur,
## method,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17910] {evalu,
## featur,
## method,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [17911] {evalu,
## featur,
## represent,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [17912] {featur,
## method,
## represent,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [17913] {evalu,
## method,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [17914] {evalu,
## method,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17915] {evalu,
## method,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [17916] {evalu,
## network,
## represent,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [17917] {method,
## network,
## represent,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [17918] {evalu,
## featur,
## method,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [17919] {evalu,
## method,
## network,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17920] {evalu,
## featur,
## method,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [17921] {evalu,
## featur,
## network,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [17922] {featur,
## method,
## network,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [17923] {approach,
## evalu,
## method,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [17924] {approach,
## evalu,
## featur,
## method} => {represent} 0.1000000 1.0000000 2.000000 3
## [17925] {evalu,
## featur,
## method,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [17926] {approach,
## evalu,
## featur,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [17927] {approach,
## featur,
## method,
## represent} => {evalu} 0.1000000 1.0000000 6.000000 3
## [17928] {approach,
## evalu,
## method,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [17929] {approach,
## evalu,
## method,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [17930] {evalu,
## method,
## network,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [17931] {approach,
## evalu,
## network,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [17932] {approach,
## method,
## network,
## represent} => {evalu} 0.1000000 1.0000000 6.000000 3
## [17933] {approach,
## evalu,
## featur,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [17934] {approach,
## evalu,
## method,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [17935] {evalu,
## featur,
## method,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [17936] {approach,
## evalu,
## featur,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [17937] {approach,
## featur,
## method,
## network} => {evalu} 0.1000000 0.7500000 4.500000 3
## [17938] {data,
## evalu,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [17939] {data,
## evalu,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [17940] {evalu,
## method,
## model,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [17941] {data,
## evalu,
## model,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [17942] {data,
## method,
## model,
## show} => {evalu} 0.1000000 1.0000000 6.000000 3
## [17943] {evalu,
## featur,
## method,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [17944] {evalu,
## method,
## network,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [17945] {evalu,
## featur,
## method,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [17946] {evalu,
## featur,
## network,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [17947] {featur,
## method,
## network,
## represent} => {evalu} 0.1000000 1.0000000 6.000000 3
## [17948] {approach,
## evalu,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17949] {approach,
## evalu,
## featur,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17950] {evalu,
## featur,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [17951] {approach,
## evalu,
## featur,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [17952] {approach,
## evalu,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [17953] {approach,
## evalu,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17954] {evalu,
## network,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [17955] {approach,
## evalu,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [17956] {approach,
## network,
## represent,
## task} => {evalu} 0.1000000 0.7500000 4.500000 3
## [17957] {approach,
## evalu,
## featur,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [17958] {approach,
## evalu,
## network,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17959] {evalu,
## featur,
## network,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [17960] {approach,
## evalu,
## featur,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [17961] {approach,
## featur,
## network,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [17962] {evalu,
## featur,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [17963] {evalu,
## network,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17964] {evalu,
## featur,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [17965] {evalu,
## featur,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [17966] {featur,
## network,
## represent,
## task} => {evalu} 0.1000000 0.7500000 4.500000 3
## [17967] {approach,
## evalu,
## featur,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [17968] {approach,
## evalu,
## network,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [17969] {approach,
## evalu,
## featur,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [17970] {evalu,
## featur,
## network,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [17971] {approach,
## featur,
## network,
## represent} => {evalu} 0.1000000 0.7500000 4.500000 3
## [17972] {evalu,
## model,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [17973] {evalu,
## network,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [17974] {evalu,
## model,
## network,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [17975] {evalu,
## model,
## network,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [17976] {model,
## network,
## represent,
## show} => {evalu} 0.1000000 0.7500000 4.500000 3
## [17977] {outperform,
## show,
## object,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [17978] {outperform,
## object,
## propos,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [17979] {outperform,
## show,
## propos,
## challeng} => {object} 0.1000000 1.0000000 3.750000 3
## [17980] {outperform,
## show,
## object,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [17981] {show,
## object,
## propos,
## challeng} => {outperform} 0.1000000 1.0000000 7.500000 3
## [17982] {data,
## outperform,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [17983] {data,
## model,
## outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [17984] {model,
## outperform,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [17985] {data,
## model,
## outperform,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [17986] {data,
## model,
## show,
## task} => {outperform} 0.1000000 0.7500000 5.625000 3
## [17987] {data,
## outperform,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17988] {data,
## featur,
## outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [17989] {featur,
## outperform,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [17990] {data,
## featur,
## outperform,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [17991] {data,
## model,
## outperform,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17992] {data,
## featur,
## outperform,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [17993] {featur,
## model,
## outperform,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [17994] {data,
## featur,
## model,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [17995] {model,
## outperform,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [17996] {featur,
## outperform,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [17997] {featur,
## model,
## outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [17998] {featur,
## model,
## outperform,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [17999] {featur,
## model,
## show,
## task} => {outperform} 0.1000000 0.7500000 5.625000 3
## [18000] {data,
## model,
## outperform,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [18001] {data,
## featur,
## outperform,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [18002] {data,
## featur,
## model,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [18003] {featur,
## model,
## outperform,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [18004] {data,
## featur,
## model,
## show} => {outperform} 0.1000000 0.7500000 5.625000 3
## [18005] {data,
## task,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [18006] {data,
## task,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18007] {task,
## dataset,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [18008] {data,
## dataset,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [18009] {data,
## task,
## dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [18010] {data,
## featur,
## task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18011] {featur,
## task,
## dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [18012] {data,
## featur,
## dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [18013] {data,
## featur,
## task,
## dataset} => {design} 0.1000000 0.7500000 5.625000 3
## [18014] {data,
## task,
## dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [18015] {data,
## network,
## task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18016] {network,
## task,
## dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [18017] {data,
## network,
## dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [18018] {data,
## network,
## task,
## dataset} => {design} 0.1000000 0.7500000 5.625000 3
## [18019] {data,
## task,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [18020] {data,
## featur,
## task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [18021] {featur,
## task,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [18022] {data,
## featur,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [18023] {data,
## task,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [18024] {data,
## network,
## task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [18025] {network,
## task,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [18026] {data,
## network,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [18027] {data,
## network,
## task,
## learn} => {design} 0.1000000 0.7500000 5.625000 3
## [18028] {data,
## featur,
## task,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [18029] {data,
## network,
## task,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [18030] {featur,
## network,
## task,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [18031] {data,
## featur,
## network,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [18032] {data,
## featur,
## network,
## task} => {design} 0.1000000 0.7500000 5.625000 3
## [18033] {task,
## dataset,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [18034] {featur,
## task,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [18035] {featur,
## task,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18036] {featur,
## dataset,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [18037] {featur,
## task,
## dataset,
## learn} => {design} 0.1000000 0.7500000 5.625000 3
## [18038] {task,
## dataset,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [18039] {network,
## task,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [18040] {network,
## task,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18041] {network,
## dataset,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [18042] {network,
## task,
## dataset,
## learn} => {design} 0.1000000 0.7500000 5.625000 3
## [18043] {featur,
## task,
## dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [18044] {network,
## task,
## dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [18045] {featur,
## network,
## task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18046] {featur,
## network,
## dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [18047] {featur,
## network,
## task,
## dataset} => {design} 0.1000000 1.0000000 7.500000 3
## [18048] {featur,
## task,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [18049] {network,
## task,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [18050] {featur,
## network,
## task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [18051] {featur,
## network,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [18052] {featur,
## network,
## task,
## learn} => {design} 0.1000000 0.7500000 5.625000 3
## [18053] {data,
## dataset,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [18054] {data,
## featur,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [18055] {data,
## featur,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18056] {featur,
## dataset,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [18057] {data,
## dataset,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [18058] {data,
## network,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [18059] {data,
## network,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18060] {network,
## dataset,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [18061] {data,
## network,
## dataset,
## learn} => {design} 0.1000000 0.7500000 5.625000 3
## [18062] {data,
## featur,
## dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [18063] {data,
## network,
## dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [18064] {data,
## featur,
## network,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18065] {featur,
## network,
## dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [18066] {data,
## featur,
## network,
## dataset} => {design} 0.1000000 0.7500000 5.625000 3
## [18067] {data,
## featur,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [18068] {data,
## network,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [18069] {data,
## featur,
## network,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [18070] {featur,
## network,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [18071] {data,
## featur,
## network,
## learn} => {design} 0.1000000 1.0000000 7.500000 3
## [18072] {featur,
## dataset,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [18073] {network,
## dataset,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [18074] {featur,
## network,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [18075] {featur,
## network,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18076] {featur,
## network,
## dataset,
## learn} => {design} 0.1000000 1.0000000 7.500000 3
## [18077] {method,
## dataset,
## perform,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [18078] {method,
## perform,
## propos,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18079] {method,
## dataset,
## propos,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [18080] {dataset,
## perform,
## propos,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [18081] {method,
## dataset,
## perform,
## propos} => {system} 0.1000000 0.7500000 4.500000 3
## [18082] {dataset,
## perform,
## propos,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [18083] {model,
## dataset,
## perform,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [18084] {model,
## perform,
## propos,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18085] {model,
## dataset,
## propos,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [18086] {model,
## dataset,
## perform,
## propos} => {system} 0.1000000 0.7500000 4.500000 3
## [18087] {dataset,
## perform,
## propos,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [18088] {featur,
## dataset,
## perform,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [18089] {featur,
## perform,
## propos,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18090] {featur,
## dataset,
## propos,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [18091] {featur,
## dataset,
## perform,
## propos} => {system} 0.1000000 0.7500000 4.500000 3
## [18092] {method,
## detect,
## propos,
## stateoftheart} => {featur} 0.1000000 1.0000000 1.875000 3
## [18093] {featur,
## method,
## detect,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [18094] {featur,
## detect,
## propos,
## stateoftheart} => {method} 0.1000000 1.0000000 2.727273 3
## [18095] {featur,
## method,
## detect,
## propos} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [18096] {featur,
## method,
## propos,
## stateoftheart} => {detect} 0.1000000 1.0000000 6.000000 3
## [18097] {method,
## appli,
## detect,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [18098] {method,
## appli,
## detect,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [18099] {appli,
## detect,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [18100] {method,
## detect,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [18101] {method,
## appli,
## perform,
## propos} => {detect} 0.1000000 0.7500000 4.500000 3
## [18102] {method,
## appli,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [18103] {featur,
## method,
## appli,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [18104] {featur,
## appli,
## detect,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [18105] {featur,
## method,
## detect,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [18106] {featur,
## method,
## appli,
## perform} => {detect} 0.1000000 1.0000000 6.000000 3
## [18107] {method,
## appli,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [18108] {featur,
## method,
## appli,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [18109] {featur,
## appli,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [18110] {featur,
## method,
## detect,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [18111] {featur,
## method,
## appli,
## propos} => {detect} 0.1000000 1.0000000 6.000000 3
## [18112] {appli,
## detect,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [18113] {featur,
## appli,
## detect,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [18114] {featur,
## appli,
## detect,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [18115] {featur,
## detect,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [18116] {featur,
## appli,
## perform,
## propos} => {detect} 0.1000000 0.7500000 4.500000 3
## [18117] {method,
## show,
## detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [18118] {method,
## detect,
## object,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [18119] {show,
## detect,
## object,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [18120] {method,
## show,
## detect,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [18121] {method,
## show,
## object,
## propos} => {detect} 0.1000000 1.0000000 6.000000 3
## [18122] {method,
## show,
## detect,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [18123] {featur,
## method,
## detect,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [18124] {featur,
## show,
## detect,
## object} => {method} 0.1000000 1.0000000 2.727273 3
## [18125] {featur,
## method,
## show,
## detect} => {object} 0.1000000 1.0000000 3.750000 3
## [18126] {featur,
## method,
## show,
## object} => {detect} 0.1000000 1.0000000 6.000000 3
## [18127] {method,
## detect,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [18128] {featur,
## method,
## detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [18129] {featur,
## detect,
## object,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [18130] {featur,
## method,
## detect,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [18131] {featur,
## method,
## object,
## propos} => {detect} 0.1000000 1.0000000 6.000000 3
## [18132] {show,
## detect,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [18133] {featur,
## show,
## detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [18134] {featur,
## detect,
## object,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [18135] {featur,
## show,
## detect,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [18136] {featur,
## show,
## object,
## propos} => {detect} 0.1000000 0.7500000 4.500000 3
## [18137] {architectur,
## dataset,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [18138] {featur,
## architectur,
## detect,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18139] {featur,
## architectur,
## dataset,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [18140] {featur,
## dataset,
## detect,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18141] {featur,
## architectur,
## dataset,
## perform} => {detect} 0.1000000 1.0000000 6.000000 3
## [18142] {architectur,
## dataset,
## detect,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [18143] {network,
## architectur,
## detect,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18144] {network,
## architectur,
## dataset,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [18145] {network,
## dataset,
## detect,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18146] {network,
## architectur,
## dataset,
## perform} => {detect} 0.1000000 0.7500000 4.500000 3
## [18147] {featur,
## architectur,
## detect,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [18148] {network,
## architectur,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [18149] {featur,
## network,
## architectur,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [18150] {featur,
## network,
## detect,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18151] {featur,
## network,
## architectur,
## perform} => {detect} 0.1000000 1.0000000 6.000000 3
## [18152] {featur,
## architectur,
## dataset,
## detect} => {network} 0.1000000 1.0000000 1.578947 3
## [18153] {network,
## architectur,
## dataset,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [18154] {featur,
## network,
## architectur,
## detect} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18155] {featur,
## network,
## dataset,
## detect} => {architectur} 0.1000000 0.7500000 2.812500 3
## [18156] {featur,
## network,
## architectur,
## dataset} => {detect} 0.1000000 0.7500000 4.500000 3
## [18157] {method,
## detect,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [18158] {featur,
## method,
## detect,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [18159] {featur,
## method,
## detect,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [18160] {featur,
## detect,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [18161] {featur,
## method,
## perform,
## propos} => {detect} 0.1000000 1.0000000 6.000000 3
## [18162] {method,
## dataset,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [18163] {featur,
## method,
## dataset,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [18164] {featur,
## method,
## detect,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [18165] {featur,
## dataset,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [18166] {featur,
## method,
## dataset,
## propos} => {detect} 0.1000000 1.0000000 6.000000 3
## [18167] {method,
## dataset,
## detect,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [18168] {method,
## network,
## dataset,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [18169] {method,
## network,
## detect,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18170] {network,
## dataset,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [18171] {method,
## network,
## dataset,
## propos} => {detect} 0.1000000 0.7500000 4.500000 3
## [18172] {featur,
## method,
## dataset,
## detect} => {network} 0.1000000 1.0000000 1.578947 3
## [18173] {method,
## network,
## dataset,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [18174] {featur,
## method,
## network,
## detect} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18175] {featur,
## network,
## dataset,
## detect} => {method} 0.1000000 0.7500000 2.045455 3
## [18176] {featur,
## method,
## network,
## dataset} => {detect} 0.1000000 1.0000000 6.000000 3
## [18177] {method,
## show,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [18178] {featur,
## method,
## show,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [18179] {featur,
## method,
## detect,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [18180] {featur,
## show,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [18181] {featur,
## method,
## show,
## propos} => {detect} 0.1000000 1.0000000 6.000000 3
## [18182] {featur,
## method,
## detect,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [18183] {method,
## network,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [18184] {featur,
## method,
## network,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [18185] {featur,
## network,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [18186] {featur,
## method,
## network,
## propos} => {detect} 0.1000000 0.7500000 4.500000 3
## [18187] {featur,
## dataset,
## detect,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18188] {network,
## dataset,
## detect,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [18189] {featur,
## network,
## detect,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18190] {featur,
## network,
## dataset,
## detect} => {work} 0.1000000 0.7500000 1.875000 3
## [18191] {featur,
## network,
## dataset,
## work} => {detect} 0.1000000 0.7500000 4.500000 3
## [18192] {featur,
## dataset,
## detect,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [18193] {network,
## dataset,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [18194] {featur,
## network,
## detect,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18195] {featur,
## network,
## dataset,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [18196] {featur,
## network,
## dataset,
## perform} => {detect} 0.1000000 1.0000000 6.000000 3
## [18197] {featur,
## dataset,
## detect,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [18198] {network,
## dataset,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [18199] {featur,
## network,
## dataset,
## detect} => {propos} 0.1000000 0.7500000 1.500000 3
## [18200] {featur,
## network,
## detect,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18201] {featur,
## network,
## dataset,
## propos} => {detect} 0.1000000 0.7500000 4.500000 3
## [18202] {represent,
## show,
## object,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [18203] {represent,
## object,
## propos,
## stateoftheart} => {show} 0.1000000 1.0000000 1.875000 3
## [18204] {show,
## object,
## propos,
## stateoftheart} => {represent} 0.1000000 1.0000000 2.000000 3
## [18205] {represent,
## show,
## propos,
## stateoftheart} => {object} 0.1000000 1.0000000 3.750000 3
## [18206] {represent,
## show,
## object,
## propos} => {stateoftheart} 0.1000000 1.0000000 6.000000 3
## [18207] {architectur,
## addit,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18208] {architectur,
## dataset,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [18209] {dataset,
## addit,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18210] {architectur,
## dataset,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18211] {architectur,
## dataset,
## addit,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [18212] {architectur,
## addit,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18213] {architectur,
## propos,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [18214] {propos,
## addit,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18215] {architectur,
## propos,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18216] {architectur,
## propos,
## addit,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [18217] {architectur,
## addit,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18218] {network,
## architectur,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [18219] {network,
## addit,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18220] {network,
## architectur,
## effici,
## work} => {addit} 0.1000000 0.7500000 4.500000 3
## [18221] {network,
## architectur,
## addit,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [18222] {architectur,
## dataset,
## addit,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [18223] {architectur,
## propos,
## addit,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18224] {dataset,
## propos,
## addit,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18225] {architectur,
## dataset,
## propos,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [18226] {architectur,
## dataset,
## propos,
## addit} => {effici} 0.1000000 1.0000000 6.000000 3
## [18227] {architectur,
## dataset,
## addit,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [18228] {network,
## architectur,
## addit,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18229] {network,
## dataset,
## addit,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18230] {network,
## architectur,
## dataset,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [18231] {network,
## architectur,
## dataset,
## addit} => {effici} 0.1000000 1.0000000 6.000000 3
## [18232] {architectur,
## propos,
## addit,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [18233] {network,
## architectur,
## addit,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [18234] {network,
## propos,
## addit,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18235] {network,
## architectur,
## propos,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [18236] {network,
## architectur,
## propos,
## addit} => {effici} 0.1000000 1.0000000 6.000000 3
## [18237] {dataset,
## addit,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18238] {propos,
## addit,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18239] {dataset,
## propos,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [18240] {dataset,
## propos,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18241] {dataset,
## propos,
## addit,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [18242] {dataset,
## addit,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18243] {network,
## addit,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18244] {network,
## dataset,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [18245] {network,
## dataset,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18246] {network,
## dataset,
## addit,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [18247] {propos,
## addit,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18248] {network,
## addit,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18249] {network,
## propos,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [18250] {network,
## propos,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18251] {network,
## propos,
## addit,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [18252] {dataset,
## propos,
## addit,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [18253] {network,
## dataset,
## addit,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [18254] {network,
## propos,
## addit,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18255] {network,
## dataset,
## propos,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [18256] {network,
## dataset,
## propos,
## addit} => {effici} 0.1000000 0.7500000 4.500000 3
## [18257] {reduc,
## comput,
## effici,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [18258] {reduc,
## comput,
## effici,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [18259] {comput,
## effici,
## optim,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [18260] {reduc,
## effici,
## optim,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [18261] {reduc,
## comput,
## optim,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [18262] {reduc,
## comput,
## effici,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [18263] {reduc,
## improv,
## comput,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [18264] {improv,
## comput,
## effici,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [18265] {reduc,
## improv,
## effici,
## optim} => {comput} 0.1000000 1.0000000 4.285714 3
## [18266] {reduc,
## improv,
## comput,
## optim} => {effici} 0.1000000 1.0000000 6.000000 3
## [18267] {reduc,
## comput,
## effici,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [18268] {reduc,
## improv,
## comput,
## effici} => {problem} 0.1000000 1.0000000 3.333333 3
## [18269] {improv,
## comput,
## effici,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [18270] {reduc,
## improv,
## effici,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [18271] {reduc,
## improv,
## comput,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [18272] {comput,
## effici,
## optim,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [18273] {improv,
## comput,
## effici,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [18274] {improv,
## comput,
## effici,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [18275] {improv,
## effici,
## optim,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [18276] {improv,
## comput,
## optim,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [18277] {reduc,
## effici,
## optim,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [18278] {reduc,
## improv,
## effici,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [18279] {reduc,
## improv,
## effici,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [18280] {improv,
## effici,
## optim,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [18281] {reduc,
## improv,
## optim,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [18282] {architectur,
## perform,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18283] {network,
## architectur,
## effici,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [18284] {network,
## architectur,
## perform,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [18285] {network,
## perform,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18286] {network,
## architectur,
## perform,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [18287] {architectur,
## dataset,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18288] {architectur,
## propos,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18289] {architectur,
## dataset,
## propos,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [18290] {dataset,
## propos,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18291] {architectur,
## dataset,
## propos,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [18292] {architectur,
## dataset,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18293] {network,
## architectur,
## effici,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [18294] {network,
## architectur,
## dataset,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [18295] {network,
## dataset,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18296] {network,
## architectur,
## dataset,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [18297] {architectur,
## propos,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18298] {network,
## architectur,
## effici,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [18299] {network,
## architectur,
## propos,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [18300] {network,
## propos,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18301] {network,
## architectur,
## propos,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [18302] {architectur,
## dataset,
## propos,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [18303] {network,
## architectur,
## dataset,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [18304] {network,
## architectur,
## propos,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18305] {network,
## dataset,
## propos,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18306] {network,
## architectur,
## dataset,
## propos} => {effici} 0.1000000 0.7500000 4.500000 3
## [18307] {dataset,
## propos,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18308] {network,
## dataset,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18309] {network,
## propos,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18310] {network,
## dataset,
## propos,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [18311] {dataset,
## addit,
## set,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18312] {propos,
## addit,
## set,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18313] {dataset,
## propos,
## addit,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [18314] {dataset,
## propos,
## set,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18315] {dataset,
## propos,
## addit,
## work} => {set} 0.1000000 0.7500000 5.625000 3
## [18316] {dataset,
## addit,
## set,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18317] {network,
## addit,
## set,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18318] {network,
## dataset,
## addit,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [18319] {network,
## dataset,
## set,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18320] {network,
## dataset,
## addit,
## work} => {set} 0.1000000 0.7500000 5.625000 3
## [18321] {propos,
## addit,
## set,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18322] {network,
## addit,
## set,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18323] {network,
## propos,
## addit,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [18324] {network,
## propos,
## set,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18325] {network,
## propos,
## addit,
## work} => {set} 0.1000000 0.7500000 5.625000 3
## [18326] {dataset,
## propos,
## addit,
## set} => {network} 0.1000000 1.0000000 1.578947 3
## [18327] {network,
## dataset,
## addit,
## set} => {propos} 0.1000000 1.0000000 2.000000 3
## [18328] {network,
## propos,
## addit,
## set} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18329] {network,
## dataset,
## propos,
## set} => {addit} 0.1000000 1.0000000 6.000000 3
## [18330] {network,
## dataset,
## propos,
## addit} => {set} 0.1000000 0.7500000 5.625000 3
## [18331] {data,
## task,
## recognit,
## set} => {learn} 0.1000000 1.0000000 2.307692 3
## [18332] {task,
## recognit,
## set,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [18333] {data,
## recognit,
## set,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [18334] {data,
## task,
## set,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [18335] {data,
## task,
## recognit,
## learn} => {set} 0.1000000 0.7500000 5.625000 3
## [18336] {dataset,
## propos,
## set,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18337] {network,
## dataset,
## set,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18338] {network,
## propos,
## set,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18339] {network,
## dataset,
## propos,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [18340] {architectur,
## neural,
## result,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [18341] {featur,
## architectur,
## result,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [18342] {featur,
## architectur,
## neural,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [18343] {featur,
## neural,
## result,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18344] {featur,
## architectur,
## neural,
## result} => {shallow} 0.1000000 1.0000000 6.000000 3
## [18345] {architectur,
## neural,
## result,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [18346] {network,
## architectur,
## result,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [18347] {network,
## architectur,
## neural,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [18348] {network,
## neural,
## result,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18349] {network,
## architectur,
## neural,
## result} => {shallow} 0.1000000 1.0000000 6.000000 3
## [18350] {featur,
## architectur,
## result,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [18351] {network,
## architectur,
## result,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [18352] {featur,
## network,
## architectur,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [18353] {featur,
## network,
## result,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18354] {featur,
## network,
## architectur,
## result} => {shallow} 0.1000000 1.0000000 6.000000 3
## [18355] {featur,
## architectur,
## neural,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [18356] {network,
## architectur,
## neural,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [18357] {featur,
## network,
## architectur,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [18358] {featur,
## network,
## neural,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18359] {featur,
## network,
## architectur,
## neural} => {shallow} 0.1000000 1.0000000 6.000000 3
## [18360] {approach,
## classif,
## method,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [18361] {classif,
## featur,
## method,
## shallow} => {approach} 0.1000000 1.0000000 2.500000 3
## [18362] {approach,
## classif,
## featur,
## shallow} => {method} 0.1000000 1.0000000 2.727273 3
## [18363] {approach,
## featur,
## method,
## shallow} => {classif} 0.1000000 1.0000000 3.750000 3
## [18364] {approach,
## classif,
## featur,
## method} => {shallow} 0.1000000 0.7500000 4.500000 3
## [18365] {dataset,
## improv,
## perform,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [18366] {featur,
## improv,
## perform,
## shallow} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18367] {featur,
## dataset,
## improv,
## shallow} => {perform} 0.1000000 1.0000000 2.142857 3
## [18368] {featur,
## dataset,
## perform,
## shallow} => {improv} 0.1000000 1.0000000 3.333333 3
## [18369] {featur,
## dataset,
## improv,
## perform} => {shallow} 0.1000000 0.7500000 4.500000 3
## [18370] {featur,
## neural,
## result,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [18371] {network,
## neural,
## result,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [18372] {featur,
## network,
## result,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [18373] {featur,
## network,
## neural,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [18374] {featur,
## network,
## neural,
## result} => {shallow} 0.1000000 1.0000000 6.000000 3
## [18375] {represent,
## work,
## semant,
## larg} => {propos} 0.1000000 1.0000000 2.000000 3
## [18376] {propos,
## work,
## semant,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [18377] {represent,
## propos,
## semant,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [18378] {represent,
## propos,
## work,
## semant} => {larg} 0.1000000 0.7500000 4.500000 3
## [18379] {represent,
## propos,
## work,
## larg} => {semant} 0.1000000 1.0000000 6.000000 3
## [18380] {approach,
## task,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18381] {approach,
## data,
## task,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18382] {data,
## task,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18383] {approach,
## data,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18384] {approach,
## data,
## task,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18385] {approach,
## task,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18386] {approach,
## task,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18387] {task,
## dataset,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18388] {approach,
## dataset,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18389] {approach,
## task,
## dataset,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18390] {approach,
## task,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18391] {approach,
## task,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18392] {task,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18393] {approach,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18394] {approach,
## task,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18395] {approach,
## task,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18396] {approach,
## represent,
## task,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18397] {represent,
## task,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18398] {approach,
## represent,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18399] {approach,
## represent,
## task,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18400] {approach,
## task,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18401] {approach,
## task,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18402] {task,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18403] {approach,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18404] {approach,
## task,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18405] {approach,
## data,
## task,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18406] {approach,
## task,
## dataset,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18407] {data,
## task,
## dataset,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18408] {approach,
## data,
## dataset,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18409] {approach,
## data,
## task,
## dataset} => {semant} 0.1000000 1.0000000 6.000000 3
## [18410] {approach,
## data,
## task,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18411] {approach,
## task,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18412] {data,
## task,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18413] {approach,
## data,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18414] {approach,
## data,
## task,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18415] {approach,
## data,
## task,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18416] {approach,
## represent,
## task,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18417] {data,
## represent,
## task,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18418] {approach,
## data,
## represent,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18419] {approach,
## data,
## task,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18420] {approach,
## task,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18421] {data,
## task,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18422] {approach,
## data,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18423] {approach,
## data,
## task,
## propos} => {semant} 0.1000000 0.7500000 4.500000 3
## [18424] {approach,
## task,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18425] {approach,
## task,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18426] {task,
## dataset,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18427] {approach,
## dataset,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18428] {approach,
## task,
## dataset,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18429] {approach,
## task,
## dataset,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18430] {approach,
## represent,
## task,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18431] {represent,
## task,
## dataset,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18432] {approach,
## represent,
## dataset,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18433] {approach,
## represent,
## task,
## dataset} => {semant} 0.1000000 1.0000000 6.000000 3
## [18434] {approach,
## task,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18435] {approach,
## task,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18436] {task,
## dataset,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18437] {approach,
## dataset,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18438] {approach,
## task,
## dataset,
## propos} => {semant} 0.1000000 1.0000000 6.000000 3
## [18439] {approach,
## task,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18440] {approach,
## represent,
## task,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18441] {represent,
## task,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18442] {approach,
## represent,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18443] {approach,
## represent,
## task,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18444] {approach,
## task,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18445] {approach,
## task,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18446] {task,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18447] {approach,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18448] {approach,
## task,
## propos,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18449] {approach,
## represent,
## task,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18450] {approach,
## task,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18451] {represent,
## task,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18452] {approach,
## represent,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18453] {data,
## task,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18454] {task,
## dataset,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18455] {data,
## task,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18456] {data,
## dataset,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18457] {data,
## task,
## dataset,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [18458] {data,
## task,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18459] {task,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18460] {data,
## task,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18461] {data,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18462] {data,
## task,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18463] {data,
## task,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18464] {represent,
## task,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18465] {data,
## represent,
## task,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18466] {data,
## represent,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18467] {data,
## represent,
## task,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18468] {data,
## task,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18469] {task,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18470] {data,
## task,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18471] {data,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18472] {data,
## task,
## propos,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [18473] {task,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18474] {task,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18475] {task,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18476] {dataset,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18477] {task,
## dataset,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18478] {task,
## dataset,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18479] {represent,
## task,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18480] {represent,
## task,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18481] {represent,
## dataset,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18482] {represent,
## task,
## dataset,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18483] {task,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18484] {task,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18485] {task,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18486] {dataset,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18487] {task,
## dataset,
## propos,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [18488] {task,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18489] {represent,
## task,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18490] {represent,
## task,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18491] {represent,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18492] {represent,
## task,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18493] {task,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18494] {task,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18495] {task,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18496] {propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18497] {task,
## propos,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18498] {represent,
## task,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18499] {task,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18500] {represent,
## task,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18501] {represent,
## propos,
## work,
## semant} => {task} 0.1000000 0.7500000 2.045455 3
## [18502] {represent,
## task,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18503] {data,
## task,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18504] {data,
## task,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18505] {task,
## dataset,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18506] {data,
## dataset,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18507] {data,
## task,
## dataset,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18508] {data,
## represent,
## task,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18509] {represent,
## task,
## dataset,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18510] {data,
## represent,
## dataset,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18511] {data,
## represent,
## task,
## dataset} => {semant} 0.1000000 0.7500000 4.500000 3
## [18512] {data,
## task,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18513] {data,
## task,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18514] {task,
## dataset,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18515] {data,
## dataset,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18516] {data,
## task,
## dataset,
## propos} => {semant} 0.1000000 0.7500000 4.500000 3
## [18517] {data,
## task,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18518] {data,
## represent,
## task,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18519] {represent,
## task,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18520] {data,
## represent,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18521] {data,
## task,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18522] {data,
## task,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18523] {task,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18524] {data,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18525] {data,
## represent,
## task,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18526] {data,
## task,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18527] {represent,
## task,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18528] {data,
## represent,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18529] {data,
## represent,
## task,
## propos} => {semant} 0.1000000 0.7500000 4.500000 3
## [18530] {task,
## dataset,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18531] {represent,
## task,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18532] {represent,
## task,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18533] {represent,
## dataset,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18534] {represent,
## task,
## dataset,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18535] {task,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18536] {task,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18537] {task,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18538] {dataset,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18539] {task,
## dataset,
## propos,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18540] {represent,
## task,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18541] {task,
## dataset,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18542] {represent,
## task,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18543] {represent,
## dataset,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18544] {represent,
## task,
## dataset,
## propos} => {semant} 0.1000000 1.0000000 6.000000 3
## [18545] {represent,
## task,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18546] {task,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18547] {represent,
## task,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18548] {represent,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [18549] {represent,
## task,
## propos,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18550] {approach,
## data,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18551] {approach,
## dataset,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18552] {approach,
## data,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18553] {data,
## dataset,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18554] {approach,
## data,
## dataset,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18555] {approach,
## data,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18556] {approach,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18557] {approach,
## data,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18558] {data,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18559] {approach,
## data,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18560] {approach,
## data,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18561] {approach,
## represent,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18562] {approach,
## data,
## represent,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18563] {data,
## represent,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18564] {approach,
## data,
## represent,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18565] {approach,
## data,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18566] {approach,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18567] {approach,
## data,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18568] {data,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18569] {approach,
## data,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18570] {approach,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18571] {approach,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18572] {approach,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18573] {dataset,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18574] {approach,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18575] {approach,
## dataset,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18576] {approach,
## represent,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18577] {approach,
## represent,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18578] {represent,
## dataset,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18579] {approach,
## represent,
## dataset,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18580] {approach,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18581] {approach,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18582] {approach,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18583] {dataset,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18584] {approach,
## dataset,
## propos,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [18585] {approach,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18586] {approach,
## represent,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18587] {approach,
## represent,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18588] {represent,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18589] {approach,
## represent,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18590] {approach,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18591] {approach,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18592] {approach,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18593] {propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18594] {approach,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18595] {approach,
## represent,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18596] {approach,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18597] {approach,
## represent,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18598] {represent,
## propos,
## work,
## semant} => {approach} 0.1000000 0.7500000 1.875000 3
## [18599] {approach,
## represent,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18600] {approach,
## data,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18601] {approach,
## data,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18602] {approach,
## dataset,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18603] {data,
## dataset,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18604] {approach,
## data,
## dataset,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18605] {approach,
## data,
## dataset,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18606] {approach,
## data,
## represent,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18607] {approach,
## represent,
## dataset,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18608] {data,
## represent,
## dataset,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18609] {approach,
## data,
## represent,
## dataset} => {semant} 0.1000000 1.0000000 6.000000 3
## [18610] {approach,
## data,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18611] {approach,
## data,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18612] {approach,
## dataset,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18613] {data,
## dataset,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18614] {approach,
## data,
## dataset,
## propos} => {semant} 0.1000000 1.0000000 6.000000 3
## [18615] {approach,
## data,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18616] {approach,
## data,
## represent,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18617] {approach,
## represent,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18618] {data,
## represent,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18619] {approach,
## data,
## represent,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18620] {approach,
## data,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18621] {approach,
## data,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18622] {approach,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18623] {data,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18624] {approach,
## data,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18625] {approach,
## data,
## represent,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18626] {approach,
## data,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18627] {approach,
## represent,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18628] {data,
## represent,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18629] {approach,
## data,
## represent,
## propos} => {semant} 0.1000000 0.7500000 4.500000 3
## [18630] {approach,
## dataset,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18631] {approach,
## represent,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18632] {approach,
## represent,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18633] {represent,
## dataset,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18634] {approach,
## represent,
## dataset,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18635] {approach,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18636] {approach,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18637] {approach,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18638] {dataset,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18639] {approach,
## dataset,
## propos,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18640] {approach,
## represent,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18641] {approach,
## dataset,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18642] {approach,
## represent,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18643] {represent,
## dataset,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18644] {approach,
## represent,
## dataset,
## propos} => {semant} 0.1000000 0.7500000 4.500000 3
## [18645] {approach,
## represent,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18646] {approach,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18647] {approach,
## represent,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18648] {represent,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [18649] {data,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18650] {data,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18651] {dataset,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18652] {data,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18653] {data,
## dataset,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18654] {data,
## dataset,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18655] {data,
## represent,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18656] {represent,
## dataset,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18657] {data,
## represent,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18658] {data,
## represent,
## dataset,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [18659] {data,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18660] {data,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18661] {dataset,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18662] {data,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18663] {data,
## dataset,
## propos,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [18664] {data,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18665] {data,
## represent,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18666] {represent,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18667] {data,
## represent,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18668] {data,
## represent,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18669] {data,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18670] {data,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18671] {propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18672] {data,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18673] {data,
## propos,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18674] {data,
## represent,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18675] {data,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18676] {represent,
## propos,
## work,
## semant} => {data} 0.1000000 0.7500000 1.730769 3
## [18677] {data,
## represent,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18678] {data,
## represent,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18679] {dataset,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18680] {represent,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18681] {represent,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18682] {represent,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18683] {represent,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18684] {dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18685] {dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18686] {propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18687] {dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18688] {dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18689] {represent,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18690] {dataset,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18691] {represent,
## propos,
## work,
## semant} => {dataset} 0.1000000 0.7500000 1.730769 3
## [18692] {represent,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18693] {represent,
## dataset,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18694] {represent,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18695] {propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18696] {represent,
## propos,
## work,
## semant} => {learn} 0.1000000 0.7500000 1.730769 3
## [18697] {represent,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18698] {represent,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [18699] {represent,
## show,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18700] {represent,
## propos,
## work,
## semant} => {show} 0.1000000 0.7500000 1.406250 3
## [18701] {show,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18702] {represent,
## show,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [18703] {represent,
## show,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [18704] {data,
## dataset,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18705] {data,
## represent,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18706] {data,
## represent,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18707] {represent,
## dataset,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18708] {data,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18709] {data,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18710] {data,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18711] {dataset,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18712] {data,
## represent,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18713] {data,
## dataset,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18714] {data,
## represent,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18715] {represent,
## dataset,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18716] {data,
## represent,
## dataset,
## propos} => {semant} 0.1000000 0.7500000 4.500000 3
## [18717] {data,
## represent,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18718] {data,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18719] {data,
## represent,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18720] {represent,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [18721] {data,
## represent,
## propos,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [18722] {represent,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [18723] {dataset,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [18724] {represent,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [18725] {represent,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18726] {architectur,
## process,
## analysi,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18727] {architectur,
## dataset,
## process,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [18728] {dataset,
## process,
## analysi,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18729] {architectur,
## dataset,
## analysi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [18730] {architectur,
## dataset,
## process,
## work} => {analysi} 0.1000000 1.0000000 7.500000 3
## [18731] {architectur,
## process,
## analysi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18732] {network,
## architectur,
## process,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [18733] {network,
## process,
## analysi,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18734] {network,
## architectur,
## analysi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [18735] {network,
## architectur,
## process,
## work} => {analysi} 0.1000000 1.0000000 7.500000 3
## [18736] {architectur,
## dataset,
## process,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [18737] {network,
## architectur,
## process,
## analysi} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18738] {network,
## dataset,
## process,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18739] {network,
## architectur,
## dataset,
## analysi} => {process} 0.1000000 1.0000000 5.000000 3
## [18740] {network,
## architectur,
## dataset,
## process} => {analysi} 0.1000000 0.7500000 5.625000 3
## [18741] {dataset,
## process,
## analysi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18742] {network,
## process,
## analysi,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18743] {network,
## dataset,
## process,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [18744] {network,
## dataset,
## analysi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [18745] {network,
## dataset,
## process,
## work} => {analysi} 0.1000000 1.0000000 7.500000 3
## [18746] {classif,
## architectur,
## experi,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [18747] {architectur,
## experi,
## propos,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [18748] {classif,
## experi,
## propos,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18749] {classif,
## architectur,
## propos,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [18750] {classif,
## architectur,
## experi,
## propos} => {analysi} 0.1000000 0.7500000 5.625000 3
## [18751] {classif,
## architectur,
## experi,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [18752] {network,
## architectur,
## experi,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [18753] {classif,
## network,
## experi,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18754] {classif,
## network,
## architectur,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [18755] {classif,
## network,
## architectur,
## experi} => {analysi} 0.1000000 0.7500000 5.625000 3
## [18756] {architectur,
## experi,
## propos,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [18757] {network,
## architectur,
## experi,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [18758] {network,
## experi,
## propos,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18759] {network,
## architectur,
## propos,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [18760] {network,
## architectur,
## experi,
## propos} => {analysi} 0.1000000 0.7500000 5.625000 3
## [18761] {classif,
## experi,
## propos,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [18762] {classif,
## network,
## experi,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [18763] {network,
## experi,
## propos,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [18764] {classif,
## network,
## propos,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [18765] {classif,
## network,
## experi,
## propos} => {analysi} 0.1000000 0.7500000 5.625000 3
## [18766] {classif,
## architectur,
## propos,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [18767] {classif,
## network,
## architectur,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [18768] {network,
## architectur,
## propos,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [18769] {classif,
## network,
## propos,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18770] {classif,
## network,
## architectur,
## propos} => {analysi} 0.1000000 0.7500000 5.625000 3
## [18771] {train,
## architectur,
## neural,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [18772] {network,
## architectur,
## neural,
## analysi} => {train} 0.1000000 1.0000000 2.500000 3
## [18773] {network,
## train,
## architectur,
## analysi} => {neural} 0.1000000 1.0000000 3.000000 3
## [18774] {network,
## train,
## neural,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18775] {network,
## train,
## architectur,
## neural} => {analysi} 0.1000000 1.0000000 7.500000 3
## [18776] {architectur,
## dataset,
## analysi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18777] {network,
## architectur,
## analysi,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18778] {network,
## architectur,
## dataset,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [18779] {network,
## dataset,
## analysi,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [18780] {network,
## architectur,
## dataset,
## work} => {analysi} 0.1000000 0.7500000 5.625000 3
## [18781] {method,
## appli,
## perform,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [18782] {method,
## appli,
## propos,
## addit} => {perform} 0.1000000 1.0000000 2.142857 3
## [18783] {appli,
## perform,
## propos,
## addit} => {method} 0.1000000 1.0000000 2.727273 3
## [18784] {method,
## perform,
## propos,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [18785] {method,
## appli,
## perform,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [18786] {appli,
## dataset,
## addit,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18787] {appli,
## propos,
## addit,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18788] {appli,
## dataset,
## propos,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [18789] {dataset,
## propos,
## addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [18790] {appli,
## dataset,
## propos,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18791] {appli,
## dataset,
## addit,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18792] {network,
## appli,
## addit,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18793] {network,
## appli,
## dataset,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [18794] {network,
## dataset,
## addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [18795] {network,
## appli,
## dataset,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18796] {appli,
## propos,
## addit,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18797] {network,
## appli,
## addit,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18798] {network,
## appli,
## propos,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [18799] {network,
## propos,
## addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [18800] {network,
## appli,
## propos,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18801] {appli,
## dataset,
## propos,
## addit} => {network} 0.1000000 1.0000000 1.578947 3
## [18802] {network,
## appli,
## dataset,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [18803] {network,
## appli,
## propos,
## addit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18804] {network,
## dataset,
## propos,
## addit} => {appli} 0.1000000 0.7500000 3.750000 3
## [18805] {network,
## appli,
## dataset,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [18806] {architectur,
## dataset,
## addit,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18807] {architectur,
## propos,
## addit,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18808] {architectur,
## dataset,
## propos,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [18809] {dataset,
## propos,
## addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [18810] {architectur,
## dataset,
## propos,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18811] {architectur,
## dataset,
## addit,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18812] {network,
## architectur,
## addit,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18813] {network,
## architectur,
## dataset,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [18814] {network,
## dataset,
## addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [18815] {network,
## architectur,
## dataset,
## work} => {addit} 0.1000000 0.7500000 4.500000 3
## [18816] {architectur,
## propos,
## addit,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [18817] {network,
## architectur,
## addit,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [18818] {network,
## architectur,
## propos,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [18819] {network,
## propos,
## addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [18820] {network,
## architectur,
## propos,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [18821] {architectur,
## dataset,
## propos,
## addit} => {network} 0.1000000 1.0000000 1.578947 3
## [18822] {network,
## architectur,
## dataset,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [18823] {network,
## architectur,
## propos,
## addit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18824] {network,
## dataset,
## propos,
## addit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [18825] {network,
## architectur,
## dataset,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [18826] {dataset,
## propos,
## addit,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [18827] {network,
## dataset,
## addit,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [18828] {network,
## propos,
## addit,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [18829] {network,
## dataset,
## propos,
## addit} => {work} 0.1333333 1.0000000 2.500000 4
## [18830] {network,
## dataset,
## propos,
## work} => {addit} 0.1333333 0.8000000 4.800000 4
## [18831] {machin,
## problem,
## recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [18832] {machin,
## show,
## recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [18833] {show,
## problem,
## recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [18834] {machin,
## show,
## problem,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [18835] {machin,
## show,
## problem,
## recent} => {function} 0.1000000 1.0000000 6.000000 3
## [18836] {machin,
## problem,
## recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [18837] {machin,
## model,
## recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [18838] {model,
## problem,
## recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [18839] {machin,
## model,
## problem,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [18840] {machin,
## model,
## problem,
## recent} => {function} 0.1000000 1.0000000 6.000000 3
## [18841] {machin,
## show,
## recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [18842] {machin,
## model,
## recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [18843] {model,
## show,
## recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [18844] {machin,
## model,
## show,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [18845] {machin,
## model,
## show,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [18846] {show,
## problem,
## recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [18847] {model,
## problem,
## recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [18848] {model,
## show,
## recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [18849] {model,
## show,
## problem,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [18850] {model,
## show,
## problem,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [18851] {machin,
## show,
## problem,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [18852] {machin,
## model,
## problem,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [18853] {machin,
## model,
## show,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [18854] {model,
## show,
## problem,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [18855] {machin,
## model,
## show,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [18856] {object,
## optim,
## problem,
## function} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [18857] {algorithm,
## object,
## optim,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [18858] {algorithm,
## optim,
## problem,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18859] {algorithm,
## object,
## problem,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18860] {algorithm,
## object,
## optim,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [18861] {task,
## object,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [18862] {data,
## object,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [18863] {data,
## task,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18864] {data,
## task,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18865] {data,
## task,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [18866] {task,
## object,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [18867] {object,
## propos,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [18868] {task,
## propos,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18869] {task,
## object,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18870] {task,
## object,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [18871] {task,
## object,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [18872] {featur,
## object,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [18873] {featur,
## task,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18874] {featur,
## task,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18875] {featur,
## task,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [18876] {data,
## object,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [18877] {object,
## propos,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [18878] {data,
## propos,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18879] {data,
## object,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18880] {data,
## object,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [18881] {data,
## object,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [18882] {featur,
## object,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [18883] {data,
## featur,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18884] {data,
## featur,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18885] {data,
## featur,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [18886] {show,
## object,
## optim,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [18887] {model,
## object,
## optim,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [18888] {model,
## show,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18889] {model,
## show,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18890] {model,
## show,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [18891] {object,
## propos,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [18892] {featur,
## object,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [18893] {featur,
## propos,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18894] {featur,
## object,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18895] {featur,
## object,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [18896] {data,
## task,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [18897] {task,
## propos,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [18898] {data,
## propos,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [18899] {data,
## task,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18900] {data,
## task,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [18901] {data,
## task,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [18902] {featur,
## task,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [18903] {data,
## featur,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [18904] {data,
## featur,
## task,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18905] {data,
## featur,
## task,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [18906] {task,
## propos,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [18907] {featur,
## task,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [18908] {featur,
## propos,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [18909] {featur,
## task,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18910] {featur,
## task,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [18911] {data,
## propos,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [18912] {data,
## featur,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [18913] {featur,
## propos,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [18914] {data,
## featur,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [18915] {data,
## featur,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [18916] {data,
## task,
## object,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [18917] {task,
## object,
## propos,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [18918] {data,
## object,
## propos,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [18919] {data,
## task,
## propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18920] {data,
## task,
## object,
## propos} => {function} 0.1000000 0.7500000 4.500000 3
## [18921] {data,
## task,
## object,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [18922] {featur,
## task,
## object,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [18923] {data,
## featur,
## object,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [18924] {data,
## featur,
## task,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18925] {data,
## featur,
## task,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [18926] {task,
## object,
## propos,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [18927] {featur,
## task,
## object,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [18928] {featur,
## object,
## propos,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [18929] {featur,
## task,
## propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18930] {featur,
## task,
## object,
## propos} => {function} 0.1000000 0.7500000 4.500000 3
## [18931] {data,
## object,
## propos,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [18932] {data,
## featur,
## object,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [18933] {featur,
## object,
## propos,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [18934] {data,
## featur,
## propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [18935] {data,
## featur,
## object,
## propos} => {function} 0.1000000 0.7500000 4.500000 3
## [18936] {data,
## task,
## propos,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [18937] {data,
## featur,
## task,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [18938] {featur,
## task,
## propos,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [18939] {data,
## featur,
## propos,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [18940] {model,
## show,
## learn,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [18941] {featur,
## show,
## learn,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [18942] {featur,
## model,
## learn,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [18943] {featur,
## model,
## show,
## function} => {learn} 0.1000000 1.0000000 2.307692 3
## [18944] {represent,
## accuraci,
## learn,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [18945] {featur,
## accuraci,
## learn,
## effect} => {represent} 0.1000000 1.0000000 2.000000 3
## [18946] {featur,
## represent,
## accuraci,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [18947] {featur,
## represent,
## accuraci,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [18948] {featur,
## represent,
## learn,
## effect} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [18949] {paper,
## train,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [18950] {paper,
## accuraci,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [18951] {train,
## accuraci,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [18952] {paper,
## train,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [18953] {paper,
## train,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [18954] {paper,
## train,
## accuraci,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [18955] {paper,
## represent,
## accuraci,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [18956] {represent,
## train,
## accuraci,
## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [18957] {paper,
## represent,
## train,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [18958] {paper,
## represent,
## train,
## achiev} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [18959] {paper,
## train,
## accuraci,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [18960] {featur,
## paper,
## accuraci,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [18961] {featur,
## train,
## accuraci,
## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [18962] {featur,
## paper,
## train,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [18963] {featur,
## paper,
## train,
## achiev} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [18964] {paper,
## accuraci,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [18965] {paper,
## represent,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [18966] {represent,
## accuraci,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [18967] {paper,
## represent,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [18968] {paper,
## represent,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [18969] {paper,
## accuraci,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [18970] {featur,
## paper,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [18971] {featur,
## accuraci,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [18972] {featur,
## paper,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [18973] {featur,
## paper,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [18974] {paper,
## represent,
## accuraci,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [18975] {featur,
## paper,
## accuraci,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [18976] {featur,
## represent,
## accuraci,
## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [18977] {featur,
## paper,
## represent,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [18978] {featur,
## paper,
## represent,
## achiev} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [18979] {accuraci,
## achiev,
## dataset,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [18980] {featur,
## accuraci,
## achiev,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [18981] {featur,
## accuraci,
## achiev,
## dataset} => {recognit} 0.1000000 1.0000000 3.333333 3
## [18982] {featur,
## accuraci,
## dataset,
## recognit} => {achiev} 0.1000000 1.0000000 4.285714 3
## [18983] {featur,
## achiev,
## dataset,
## recognit} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [18984] {featur,
## accuraci,
## achiev,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [18985] {network,
## accuraci,
## achiev,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [18986] {featur,
## network,
## accuraci,
## achiev} => {result} 0.1000000 1.0000000 3.000000 3
## [18987] {featur,
## network,
## accuraci,
## result} => {achiev} 0.1000000 1.0000000 4.285714 3
## [18988] {featur,
## network,
## achiev,
## result} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [18989] {accuraci,
## achiev,
## neural,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [18990] {featur,
## accuraci,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [18991] {featur,
## accuraci,
## achiev,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [18992] {featur,
## accuraci,
## neural,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [18993] {featur,
## achiev,
## neural,
## propos} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [18994] {train,
## accuraci,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [18995] {represent,
## train,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [18996] {represent,
## accuraci,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [18997] {represent,
## train,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [18998] {represent,
## train,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [18999] {train,
## accuraci,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [19000] {featur,
## train,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [19001] {featur,
## accuraci,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [19002] {featur,
## train,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19003] {featur,
## train,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [19004] {represent,
## train,
## accuraci,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [19005] {featur,
## train,
## accuraci,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [19006] {featur,
## represent,
## accuraci,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [19007] {featur,
## represent,
## train,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19008] {featur,
## represent,
## train,
## achiev} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [19009] {represent,
## accuraci,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [19010] {featur,
## accuraci,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [19011] {featur,
## represent,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [19012] {featur,
## represent,
## accuraci,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [19013] {featur,
## represent,
## achiev,
## learn} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [19014] {classif,
## method,
## accuraci,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [19015] {classif,
## featur,
## method,
## accuraci} => {propos} 0.1000000 1.0000000 2.000000 3
## [19016] {classif,
## featur,
## accuraci,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19017] {featur,
## method,
## accuraci,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [19018] {classif,
## featur,
## method,
## propos} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [19019] {paper,
## train,
## accuraci,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [19020] {paper,
## represent,
## train,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [19021] {paper,
## represent,
## accuraci,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [19022] {represent,
## train,
## accuraci,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [19023] {paper,
## represent,
## train,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [19024] {paper,
## train,
## accuraci,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [19025] {featur,
## paper,
## train,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [19026] {featur,
## paper,
## accuraci,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [19027] {featur,
## train,
## accuraci,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [19028] {featur,
## paper,
## train,
## learn} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [19029] {paper,
## represent,
## train,
## accuraci} => {featur} 0.1000000 1.0000000 1.875000 3
## [19030] {featur,
## paper,
## train,
## accuraci} => {represent} 0.1000000 1.0000000 2.000000 3
## [19031] {featur,
## paper,
## represent,
## accuraci} => {train} 0.1000000 1.0000000 2.500000 3
## [19032] {featur,
## represent,
## train,
## accuraci} => {paper} 0.1000000 1.0000000 3.000000 3
## [19033] {featur,
## paper,
## represent,
## train} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [19034] {paper,
## represent,
## accuraci,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [19035] {featur,
## paper,
## accuraci,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [19036] {featur,
## paper,
## represent,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [19037] {featur,
## represent,
## accuraci,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [19038] {featur,
## paper,
## represent,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [19039] {accuraci,
## perform,
## propos,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [19040] {featur,
## accuraci,
## perform,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [19041] {featur,
## accuraci,
## propos,
## recognit} => {perform} 0.1000000 1.0000000 2.142857 3
## [19042] {featur,
## accuraci,
## perform,
## propos} => {recognit} 0.1000000 1.0000000 3.333333 3
## [19043] {featur,
## perform,
## propos,
## recognit} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [19044] {represent,
## accuraci,
## recognit,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [19045] {featur,
## accuraci,
## recognit,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [19046] {featur,
## represent,
## accuraci,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [19047] {featur,
## represent,
## accuraci,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [19048] {featur,
## represent,
## recognit,
## learn} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [19049] {represent,
## train,
## accuraci,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [19050] {featur,
## train,
## accuraci,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [19051] {featur,
## represent,
## train,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [19052] {featur,
## represent,
## accuraci,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [19053] {featur,
## represent,
## train,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [19054] {represent,
## accuraci,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [19055] {featur,
## represent,
## accuraci,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [19056] {featur,
## accuraci,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [19057] {featur,
## represent,
## accuraci,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [19058] {approach,
## train,
## neural,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [19059] {train,
## neural,
## propos,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [19060] {approach,
## neural,
## propos,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [19061] {approach,
## train,
## propos,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [19062] {approach,
## train,
## neural,
## propos} => {base} 0.1000000 0.7500000 4.500000 3
## [19063] {approach,
## train,
## neural,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [19064] {network,
## train,
## neural,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [19065] {approach,
## network,
## neural,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [19066] {approach,
## network,
## train,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [19067] {approach,
## network,
## train,
## neural} => {base} 0.1000000 0.7500000 4.500000 3
## [19068] {train,
## neural,
## propos,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [19069] {network,
## train,
## neural,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [19070] {network,
## neural,
## propos,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [19071] {network,
## train,
## propos,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [19072] {network,
## train,
## neural,
## propos} => {base} 0.1000000 0.7500000 4.500000 3
## [19073] {approach,
## neural,
## propos,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [19074] {approach,
## network,
## neural,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [19075] {network,
## neural,
## propos,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [19076] {approach,
## network,
## propos,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [19077] {approach,
## train,
## propos,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [19078] {approach,
## network,
## train,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [19079] {network,
## train,
## propos,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [19080] {approach,
## network,
## propos,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [19081] {approach,
## network,
## train,
## propos} => {base} 0.1000000 0.7500000 4.500000 3
## [19082] {neural,
## result,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [19083] {train,
## result,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [19084] {train,
## neural,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [19085] {train,
## neural,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [19086] {train,
## neural,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [19087] {neural,
## result,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19088] {dataset,
## result,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [19089] {dataset,
## neural,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [19090] {dataset,
## neural,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [19091] {dataset,
## neural,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [19092] {neural,
## result,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [19093] {network,
## result,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [19094] {network,
## neural,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [19095] {network,
## neural,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [19096] {network,
## neural,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [19097] {train,
## result,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19098] {dataset,
## result,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [19099] {train,
## dataset,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [19100] {train,
## dataset,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [19101] {train,
## dataset,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [19102] {train,
## result,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [19103] {network,
## result,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [19104] {network,
## train,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [19105] {network,
## train,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [19106] {network,
## train,
## result,
## specif} => {potenti} 0.1000000 0.7500000 4.500000 3
## [19107] {dataset,
## result,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [19108] {network,
## result,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19109] {network,
## dataset,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [19110] {network,
## dataset,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [19111] {network,
## dataset,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [19112] {train,
## neural,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19113] {dataset,
## neural,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [19114] {train,
## dataset,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [19115] {train,
## dataset,
## neural,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [19116] {train,
## dataset,
## neural,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [19117] {train,
## neural,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [19118] {network,
## neural,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [19119] {network,
## train,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [19120] {network,
## train,
## neural,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [19121] {network,
## train,
## neural,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [19122] {dataset,
## neural,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [19123] {network,
## neural,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19124] {network,
## dataset,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [19125] {network,
## dataset,
## neural,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [19126] {network,
## dataset,
## neural,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [19127] {train,
## dataset,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [19128] {network,
## train,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19129] {network,
## dataset,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [19130] {network,
## train,
## dataset,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [19131] {network,
## train,
## dataset,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [19132] {result,
## layer,
## applic,
## potenti} => {data} 0.1000000 1.0000000 2.307692 3
## [19133] {data,
## layer,
## applic,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [19134] {data,
## result,
## layer,
## potenti} => {applic} 0.1000000 1.0000000 4.285714 3
## [19135] {data,
## result,
## applic,
## potenti} => {layer} 0.1000000 1.0000000 5.000000 3
## [19136] {data,
## result,
## layer,
## applic} => {potenti} 0.1000000 1.0000000 6.000000 3
## [19137] {train,
## neural,
## result,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19138] {dataset,
## neural,
## result,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [19139] {train,
## dataset,
## result,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [19140] {train,
## dataset,
## neural,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [19141] {train,
## dataset,
## neural,
## result} => {potenti} 0.1000000 1.0000000 6.000000 3
## [19142] {train,
## neural,
## result,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [19143] {network,
## neural,
## result,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [19144] {network,
## train,
## result,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [19145] {network,
## train,
## neural,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [19146] {network,
## train,
## neural,
## result} => {potenti} 0.1000000 0.7500000 4.500000 3
## [19147] {dataset,
## neural,
## result,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [19148] {network,
## neural,
## result,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19149] {network,
## dataset,
## result,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [19150] {network,
## dataset,
## neural,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [19151] {network,
## dataset,
## neural,
## result} => {potenti} 0.1000000 0.7500000 4.500000 3
## [19152] {train,
## dataset,
## result,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [19153] {network,
## train,
## result,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19154] {network,
## dataset,
## result,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [19155] {network,
## train,
## dataset,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [19156] {network,
## train,
## dataset,
## result} => {potenti} 0.1000000 0.7500000 4.500000 3
## [19157] {train,
## dataset,
## neural,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [19158] {network,
## train,
## neural,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19159] {network,
## dataset,
## neural,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [19160] {network,
## train,
## dataset,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [19161] {network,
## train,
## dataset,
## neural} => {potenti} 0.1000000 0.7500000 4.500000 3
## [19162] {data,
## dataset,
## work,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [19163] {data,
## network,
## work,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19164] {network,
## dataset,
## work,
## potenti} => {data} 0.1000000 1.0000000 2.307692 3
## [19165] {data,
## network,
## dataset,
## potenti} => {work} 0.1000000 1.0000000 2.500000 3
## [19166] {data,
## network,
## dataset,
## work} => {potenti} 0.1000000 0.7500000 4.500000 3
## [19167] {approach,
## make,
## problem,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [19168] {make,
## perform,
## problem,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [19169] {approach,
## make,
## perform,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [19170] {approach,
## perform,
## problem,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [19171] {approach,
## make,
## perform,
## problem} => {solv} 0.1000000 0.7500000 3.750000 3
## [19172] {approach,
## make,
## problem,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [19173] {featur,
## make,
## problem,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [19174] {approach,
## featur,
## make,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [19175] {approach,
## featur,
## problem,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [19176] {approach,
## featur,
## make,
## problem} => {solv} 0.1000000 1.0000000 5.000000 3
## [19177] {make,
## perform,
## problem,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [19178] {featur,
## make,
## problem,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [19179] {featur,
## make,
## perform,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [19180] {featur,
## perform,
## problem,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [19181] {featur,
## make,
## perform,
## problem} => {solv} 0.1000000 1.0000000 5.000000 3
## [19182] {approach,
## make,
## perform,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [19183] {approach,
## featur,
## make,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [19184] {featur,
## make,
## perform,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [19185] {approach,
## featur,
## perform,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [19186] {approach,
## featur,
## make,
## perform} => {solv} 0.1000000 1.0000000 5.000000 3
## [19187] {approach,
## perform,
## problem,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [19188] {approach,
## featur,
## problem,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [19189] {featur,
## perform,
## problem,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [19190] {approach,
## featur,
## perform,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [19191] {approach,
## featur,
## perform,
## problem} => {solv} 0.1000000 1.0000000 5.000000 3
## [19192] {data,
## represent,
## task,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [19193] {data,
## featur,
## task,
## solv} => {represent} 0.1000000 1.0000000 2.000000 3
## [19194] {featur,
## represent,
## task,
## solv} => {data} 0.1000000 1.0000000 2.307692 3
## [19195] {data,
## featur,
## represent,
## solv} => {task} 0.1000000 1.0000000 2.727273 3
## [19196] {train,
## result,
## layer,
## specif} => {work} 0.1000000 1.0000000 2.500000 3
## [19197] {result,
## work,
## layer,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [19198] {train,
## work,
## layer,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [19199] {train,
## result,
## work,
## specif} => {layer} 0.1000000 1.0000000 5.000000 3
## [19200] {train,
## result,
## work,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [19201] {train,
## result,
## layer,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [19202] {network,
## result,
## layer,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [19203] {network,
## train,
## layer,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [19204] {network,
## train,
## result,
## specif} => {layer} 0.1000000 0.7500000 3.750000 3
## [19205] {network,
## train,
## result,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [19206] {result,
## work,
## layer,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [19207] {network,
## result,
## layer,
## specif} => {work} 0.1000000 1.0000000 2.500000 3
## [19208] {network,
## work,
## layer,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [19209] {network,
## result,
## work,
## specif} => {layer} 0.1000000 1.0000000 5.000000 3
## [19210] {network,
## result,
## work,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [19211] {train,
## work,
## layer,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [19212] {network,
## train,
## layer,
## specif} => {work} 0.1000000 1.0000000 2.500000 3
## [19213] {network,
## work,
## layer,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [19214] {network,
## train,
## work,
## specif} => {layer} 0.1000000 1.0000000 5.000000 3
## [19215] {network,
## train,
## work,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [19216] {train,
## neural,
## result,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19217] {dataset,
## neural,
## result,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [19218] {train,
## dataset,
## result,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [19219] {train,
## dataset,
## neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [19220] {train,
## dataset,
## neural,
## result} => {specif} 0.1000000 1.0000000 6.000000 3
## [19221] {train,
## neural,
## result,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [19222] {network,
## neural,
## result,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [19223] {network,
## train,
## result,
## specif} => {neural} 0.1000000 0.7500000 2.250000 3
## [19224] {network,
## train,
## neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [19225] {network,
## train,
## neural,
## result} => {specif} 0.1000000 0.7500000 4.500000 3
## [19226] {dataset,
## neural,
## result,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [19227] {network,
## neural,
## result,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19228] {network,
## dataset,
## result,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [19229] {network,
## dataset,
## neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [19230] {network,
## dataset,
## neural,
## result} => {specif} 0.1000000 0.7500000 4.500000 3
## [19231] {train,
## algorithm,
## result,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [19232] {network,
## algorithm,
## result,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [19233] {network,
## train,
## result,
## specif} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [19234] {network,
## train,
## algorithm,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [19235] {network,
## train,
## algorithm,
## result} => {specif} 0.1000000 1.0000000 6.000000 3
## [19236] {train,
## result,
## work,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [19237] {network,
## train,
## result,
## specif} => {work} 0.1000000 0.7500000 1.875000 3
## [19238] {network,
## result,
## work,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [19239] {network,
## train,
## work,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [19240] {network,
## train,
## result,
## work} => {specif} 0.1000000 1.0000000 6.000000 3
## [19241] {train,
## dataset,
## result,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [19242] {network,
## train,
## result,
## specif} => {dataset} 0.1000000 0.7500000 1.730769 3
## [19243] {network,
## dataset,
## result,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [19244] {network,
## train,
## dataset,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [19245] {network,
## train,
## dataset,
## result} => {specif} 0.1000000 0.7500000 4.500000 3
## [19246] {train,
## dataset,
## neural,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [19247] {network,
## train,
## neural,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19248] {network,
## dataset,
## neural,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [19249] {network,
## train,
## dataset,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [19250] {network,
## train,
## dataset,
## neural} => {specif} 0.1000000 0.7500000 4.500000 3
## [19251] {represent,
## architectur,
## result,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [19252] {featur,
## architectur,
## result,
## techniqu} => {represent} 0.1000000 1.0000000 2.000000 3
## [19253] {featur,
## represent,
## architectur,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [19254] {featur,
## represent,
## result,
## techniqu} => {architectur} 0.1000000 1.0000000 3.750000 3
## [19255] {featur,
## represent,
## architectur,
## result} => {techniqu} 0.1000000 1.0000000 6.000000 3
## [19256] {appli,
## object,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [19257] {featur,
## appli,
## object,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19258] {featur,
## appli,
## object,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19259] {featur,
## appli,
## perform,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [19260] {featur,
## object,
## perform,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19261] {method,
## appli,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19262] {method,
## appli,
## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [19263] {appli,
## architectur,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [19264] {method,
## appli,
## dataset,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [19265] {method,
## architectur,
## dataset,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [19266] {method,
## appli,
## architectur,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19267] {method,
## appli,
## architectur,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19268] {appli,
## architectur,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19269] {method,
## appli,
## perform,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [19270] {method,
## architectur,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19271] {method,
## appli,
## architectur,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [19272] {method,
## network,
## appli,
## architectur} => {perform} 0.1000000 1.0000000 2.142857 3
## [19273] {network,
## appli,
## architectur,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [19274] {method,
## network,
## appli,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [19275] {method,
## network,
## architectur,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [19276] {method,
## appli,
## architectur,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [19277] {method,
## appli,
## architectur,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19278] {appli,
## architectur,
## dataset,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19279] {method,
## appli,
## dataset,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [19280] {method,
## architectur,
## dataset,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19281] {method,
## appli,
## architectur,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [19282] {method,
## network,
## appli,
## architectur} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19283] {network,
## appli,
## architectur,
## dataset} => {method} 0.1000000 1.0000000 2.727273 3
## [19284] {method,
## network,
## appli,
## dataset} => {architectur} 0.1000000 1.0000000 3.750000 3
## [19285] {method,
## network,
## architectur,
## dataset} => {appli} 0.1000000 1.0000000 5.000000 3
## [19286] {method,
## appli,
## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19287] {method,
## network,
## appli,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [19288] {network,
## appli,
## architectur,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19289] {method,
## network,
## appli,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [19290] {method,
## network,
## architectur,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19291] {appli,
## architectur,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19292] {appli,
## architectur,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19293] {appli,
## architectur,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19294] {appli,
## dataset,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [19295] {architectur,
## dataset,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19296] {appli,
## architectur,
## dataset,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [19297] {network,
## appli,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19298] {network,
## appli,
## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [19299] {network,
## appli,
## dataset,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [19300] {network,
## architectur,
## dataset,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [19301] {appli,
## architectur,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19302] {network,
## appli,
## architectur,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19303] {network,
## appli,
## architectur,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19304] {network,
## appli,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [19305] {network,
## architectur,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19306] {appli,
## architectur,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19307] {network,
## appli,
## architectur,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [19308] {network,
## appli,
## architectur,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19309] {network,
## appli,
## dataset,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [19310] {network,
## architectur,
## dataset,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19311] {classif,
## method,
## appli,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [19312] {classif,
## method,
## show,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [19313] {classif,
## show,
## appli,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [19314] {method,
## show,
## appli,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [19315] {classif,
## method,
## show,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [19316] {classif,
## method,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19317] {classif,
## method,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19318] {classif,
## appli,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19319] {method,
## appli,
## perform,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [19320] {classif,
## method,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19321] {classif,
## method,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [19322] {classif,
## method,
## appli,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19323] {classif,
## show,
## appli,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19324] {method,
## show,
## appli,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [19325] {classif,
## method,
## show,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19326] {classif,
## show,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19327] {classif,
## appli,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19328] {classif,
## show,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19329] {show,
## appli,
## perform,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [19330] {classif,
## show,
## perform,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19331] {approach,
## appli,
## dataset,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [19332] {approach,
## show,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19333] {show,
## appli,
## dataset,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [19334] {approach,
## show,
## appli,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [19335] {approach,
## show,
## dataset,
## neural} => {appli} 0.1000000 1.0000000 5.000000 3
## [19336] {approach,
## appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19337] {approach,
## appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19338] {appli,
## dataset,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19339] {approach,
## appli,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [19340] {approach,
## dataset,
## neural,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19341] {approach,
## appli,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [19342] {approach,
## network,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19343] {network,
## appli,
## dataset,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [19344] {approach,
## network,
## appli,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [19345] {approach,
## network,
## dataset,
## neural} => {appli} 0.1000000 0.7500000 3.750000 3
## [19346] {approach,
## show,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19347] {approach,
## appli,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19348] {show,
## appli,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19349] {approach,
## show,
## appli,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [19350] {approach,
## show,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19351] {approach,
## show,
## appli,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [19352] {approach,
## network,
## appli,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [19353] {network,
## show,
## appli,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [19354] {approach,
## network,
## show,
## appli} => {neural} 0.1000000 1.0000000 3.000000 3
## [19355] {approach,
## network,
## show,
## neural} => {appli} 0.1000000 0.7500000 3.750000 3
## [19356] {approach,
## appli,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19357] {approach,
## network,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19358] {network,
## appli,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19359] {approach,
## network,
## appli,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [19360] {show,
## appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19361] {appli,
## dataset,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19362] {show,
## appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19363] {show,
## appli,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [19364] {show,
## dataset,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19365] {show,
## appli,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [19366] {network,
## appli,
## dataset,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [19367] {network,
## show,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19368] {network,
## show,
## appli,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [19369] {network,
## show,
## dataset,
## neural} => {appli} 0.1000000 1.0000000 5.000000 3
## [19370] {appli,
## dataset,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19371] {network,
## appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19372] {network,
## appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19373] {network,
## appli,
## dataset,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [19374] {network,
## dataset,
## neural,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19375] {show,
## appli,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19376] {network,
## show,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19377] {network,
## appli,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19378] {network,
## show,
## appli,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [19379] {network,
## show,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19380] {method,
## appli,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19381] {method,
## appli,
## perform,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [19382] {method,
## appli,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19383] {appli,
## dataset,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19384] {method,
## dataset,
## perform,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19385] {method,
## appli,
## dataset,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [19386] {method,
## network,
## appli,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19387] {method,
## network,
## appli,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [19388] {network,
## appli,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [19389] {method,
## network,
## dataset,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [19390] {method,
## show,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19391] {method,
## appli,
## perform,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [19392] {method,
## show,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19393] {show,
## appli,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19394] {method,
## show,
## perform,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19395] {method,
## appli,
## perform,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [19396] {featur,
## method,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19397] {featur,
## method,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19398] {featur,
## appli,
## perform,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [19399] {featur,
## method,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19400] {method,
## appli,
## perform,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [19401] {method,
## network,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19402] {method,
## network,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19403] {network,
## appli,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19404] {method,
## network,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19405] {method,
## appli,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19406] {method,
## network,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [19407] {method,
## network,
## appli,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19408] {network,
## appli,
## dataset,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [19409] {method,
## network,
## dataset,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19410] {approach,
## algorithm,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19411] {approach,
## algorithm,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19412] {algorithm,
## appli,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19413] {approach,
## appli,
## perform,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [19414] {approach,
## algorithm,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19415] {approach,
## show,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [19416] {approach,
## appli,
## dataset,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19417] {approach,
## show,
## appli,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19418] {show,
## appli,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19419] {approach,
## show,
## appli,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [19420] {approach,
## network,
## appli,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [19421] {approach,
## network,
## show,
## appli} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19422] {network,
## show,
## appli,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [19423] {approach,
## network,
## show,
## dataset} => {appli} 0.1000000 0.7500000 3.750000 3
## [19424] {approach,
## appli,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19425] {approach,
## network,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [19426] {approach,
## network,
## appli,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19427] {network,
## appli,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [19428] {approach,
## show,
## appli,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19429] {approach,
## network,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [19430] {approach,
## network,
## appli,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19431] {network,
## show,
## appli,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19432] {approach,
## network,
## show,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19433] {appli,
## dataset,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [19434] {network,
## appli,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [19435] {network,
## appli,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19436] {network,
## appli,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [19437] {appli,
## dataset,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19438] {network,
## appli,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19439] {network,
## appli,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19440] {network,
## appli,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [19441] {network,
## dataset,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19442] {show,
## appli,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19443] {network,
## show,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [19444] {network,
## appli,
## dataset,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [19445] {network,
## show,
## appli,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19446] {network,
## show,
## dataset,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19447] {data,
## dataset,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [19448] {data,
## represent,
## work,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19449] {represent,
## dataset,
## work,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [19450] {data,
## represent,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [19451] {data,
## represent,
## dataset,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [19452] {data,
## dataset,
## work,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [19453] {data,
## featur,
## work,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19454] {featur,
## dataset,
## work,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [19455] {data,
## featur,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [19456] {data,
## featur,
## dataset,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [19457] {data,
## represent,
## work,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [19458] {data,
## featur,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [19459] {featur,
## represent,
## work,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [19460] {data,
## featur,
## represent,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [19461] {data,
## featur,
## represent,
## work} => {larg} 0.1000000 1.0000000 6.000000 3
## [19462] {represent,
## dataset,
## work,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [19463] {featur,
## dataset,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [19464] {featur,
## represent,
## work,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19465] {featur,
## represent,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [19466] {featur,
## represent,
## dataset,
## work} => {larg} 0.1000000 1.0000000 6.000000 3
## [19467] {data,
## represent,
## dataset,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [19468] {data,
## featur,
## dataset,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [19469] {data,
## featur,
## represent,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19470] {featur,
## represent,
## dataset,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [19471] {data,
## achiev,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19472] {data,
## achiev,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19473] {achiev,
## dataset,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19474] {data,
## dataset,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19475] {data,
## achiev,
## dataset,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19476] {data,
## achiev,
## dataset,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [19477] {data,
## represent,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19478] {represent,
## achiev,
## dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19479] {data,
## represent,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19480] {data,
## represent,
## achiev,
## dataset} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19481] {data,
## achiev,
## dataset,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19482] {data,
## featur,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19483] {featur,
## achiev,
## dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19484] {data,
## featur,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19485] {data,
## featur,
## achiev,
## dataset} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19486] {data,
## achiev,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [19487] {data,
## represent,
## achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19488] {represent,
## achiev,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19489] {data,
## represent,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19490] {data,
## represent,
## achiev,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19491] {data,
## achiev,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19492] {data,
## featur,
## achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19493] {featur,
## achiev,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19494] {data,
## featur,
## learn,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [19495] {data,
## featur,
## achiev,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19496] {data,
## represent,
## achiev,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19497] {data,
## featur,
## achiev,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [19498] {featur,
## represent,
## achiev,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19499] {data,
## featur,
## represent,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19500] {data,
## featur,
## represent,
## achiev} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19501] {achiev,
## dataset,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [19502] {represent,
## achiev,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19503] {represent,
## achiev,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19504] {represent,
## dataset,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19505] {represent,
## achiev,
## dataset,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19506] {achiev,
## dataset,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19507] {featur,
## achiev,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19508] {featur,
## achiev,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19509] {featur,
## dataset,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19510] {featur,
## achiev,
## dataset,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19511] {represent,
## achiev,
## dataset,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19512] {featur,
## achiev,
## dataset,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [19513] {featur,
## represent,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19514] {featur,
## represent,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19515] {featur,
## represent,
## achiev,
## dataset} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19516] {represent,
## achiev,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19517] {featur,
## achiev,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [19518] {featur,
## represent,
## achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19519] {featur,
## represent,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19520] {featur,
## represent,
## achiev,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [19521] {data,
## paper,
## train,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19522] {paper,
## train,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19523] {data,
## paper,
## learn,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [19524] {data,
## train,
## learn,
## challeng} => {paper} 0.1000000 1.0000000 3.000000 3
## [19525] {data,
## paper,
## train,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19526] {data,
## paper,
## train,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19527] {featur,
## paper,
## train,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19528] {data,
## featur,
## paper,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [19529] {data,
## featur,
## train,
## challeng} => {paper} 0.1000000 1.0000000 3.000000 3
## [19530] {data,
## featur,
## paper,
## train} => {challeng} 0.1000000 0.7500000 4.500000 3
## [19531] {paper,
## train,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19532] {featur,
## paper,
## train,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19533] {featur,
## paper,
## learn,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [19534] {featur,
## train,
## learn,
## challeng} => {paper} 0.1000000 1.0000000 3.000000 3
## [19535] {featur,
## paper,
## train,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [19536] {data,
## paper,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19537] {data,
## featur,
## paper,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19538] {featur,
## paper,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19539] {data,
## featur,
## learn,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [19540] {data,
## featur,
## paper,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19541] {data,
## task,
## learn,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [19542] {data,
## show,
## task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19543] {show,
## task,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19544] {data,
## show,
## learn,
## challeng} => {task} 0.1000000 1.0000000 2.727273 3
## [19545] {data,
## task,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19546] {data,
## featur,
## task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19547] {featur,
## task,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19548] {data,
## featur,
## learn,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [19549] {data,
## show,
## task,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19550] {data,
## featur,
## task,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [19551] {featur,
## show,
## task,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19552] {data,
## featur,
## show,
## challeng} => {task} 0.1000000 1.0000000 2.727273 3
## [19553] {show,
## task,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19554] {featur,
## task,
## learn,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [19555] {featur,
## show,
## task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19556] {featur,
## show,
## learn,
## challeng} => {task} 0.1000000 1.0000000 2.727273 3
## [19557] {featur,
## show,
## task,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [19558] {data,
## train,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19559] {data,
## featur,
## train,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19560] {featur,
## train,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19561] {data,
## featur,
## learn,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [19562] {data,
## featur,
## train,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19563] {data,
## dataset,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [19564] {data,
## represent,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19565] {data,
## represent,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19566] {represent,
## dataset,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19567] {data,
## dataset,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19568] {data,
## featur,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19569] {data,
## featur,
## learn,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [19570] {featur,
## dataset,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19571] {data,
## represent,
## dataset,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19572] {data,
## featur,
## dataset,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [19573] {data,
## featur,
## represent,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19574] {featur,
## represent,
## dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19575] {data,
## represent,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19576] {data,
## featur,
## learn,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [19577] {data,
## featur,
## represent,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19578] {featur,
## represent,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19579] {data,
## show,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19580] {data,
## featur,
## learn,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [19581] {data,
## featur,
## show,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19582] {featur,
## show,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19583] {data,
## featur,
## show,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [19584] {data,
## propos,
## learn,
## challeng} => {model} 0.1000000 1.0000000 1.875000 3
## [19585] {data,
## model,
## learn,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [19586] {data,
## model,
## propos,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19587] {model,
## propos,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19588] {data,
## propos,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19589] {data,
## featur,
## learn,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [19590] {data,
## featur,
## propos,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19591] {featur,
## propos,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19592] {data,
## model,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19593] {data,
## featur,
## learn,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [19594] {data,
## featur,
## model,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19595] {featur,
## model,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19596] {data,
## model,
## propos,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19597] {data,
## featur,
## propos,
## challeng} => {model} 0.1000000 1.0000000 1.875000 3
## [19598] {data,
## featur,
## model,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [19599] {featur,
## model,
## propos,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19600] {represent,
## dataset,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19601] {featur,
## dataset,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [19602] {featur,
## represent,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19603] {featur,
## represent,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19604] {featur,
## represent,
## dataset,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [19605] {model,
## propos,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19606] {featur,
## propos,
## learn,
## challeng} => {model} 0.1000000 1.0000000 1.875000 3
## [19607] {featur,
## model,
## learn,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [19608] {featur,
## model,
## propos,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19609] {represent,
## perform,
## recognit,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [19610] {perform,
## propos,
## recognit,
## imag} => {represent} 0.1000000 1.0000000 2.000000 3
## [19611] {represent,
## propos,
## recognit,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [19612] {represent,
## perform,
## propos,
## imag} => {recognit} 0.1000000 1.0000000 3.333333 3
## [19613] {represent,
## perform,
## propos,
## recognit} => {imag} 0.1000000 1.0000000 6.000000 3
## [19614] {propos,
## recognit,
## imag,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [19615] {featur,
## recognit,
## imag,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [19616] {featur,
## propos,
## recognit,
## imag} => {learn} 0.1000000 1.0000000 2.307692 3
## [19617] {featur,
## propos,
## imag,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [19618] {featur,
## propos,
## recognit,
## learn} => {imag} 0.1000000 1.0000000 6.000000 3
## [19619] {train,
## improv,
## perform,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [19620] {train,
## improv,
## propos,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [19621] {improv,
## perform,
## propos,
## imag} => {train} 0.1000000 1.0000000 2.500000 3
## [19622] {train,
## perform,
## propos,
## imag} => {improv} 0.1000000 1.0000000 3.333333 3
## [19623] {train,
## improv,
## perform,
## propos} => {imag} 0.1000000 1.0000000 6.000000 3
## [19624] {reduc,
## exist,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [19625] {network,
## reduc,
## exist,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [19626] {network,
## reduc,
## exist,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [19627] {network,
## exist,
## optim,
## work} => {reduc} 0.1000000 1.0000000 4.285714 3
## [19628] {network,
## reduc,
## optim,
## work} => {exist} 0.1000000 1.0000000 6.000000 3
## [19629] {approach,
## show,
## exist,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [19630] {approach,
## network,
## exist,
## work} => {show} 0.1000000 1.0000000 1.875000 3
## [19631] {approach,
## network,
## show,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [19632] {network,
## show,
## exist,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [19633] {approach,
## network,
## show,
## work} => {exist} 0.1000000 0.7500000 4.500000 3
## [19634] {model,
## represent,
## exist,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [19635] {featur,
## represent,
## exist,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [19636] {featur,
## model,
## exist,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [19637] {featur,
## model,
## represent,
## exist} => {learn} 0.1000000 1.0000000 2.307692 3
## [19638] {represent,
## algorithm,
## process,
## layer} => {featur} 0.1000000 1.0000000 1.875000 3
## [19639] {featur,
## algorithm,
## process,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [19640] {featur,
## represent,
## process,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [19641] {featur,
## represent,
## algorithm,
## layer} => {process} 0.1000000 1.0000000 5.000000 3
## [19642] {featur,
## represent,
## algorithm,
## process} => {layer} 0.1000000 1.0000000 5.000000 3
## [19643] {train,
## result,
## work,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [19644] {network,
## train,
## result,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [19645] {network,
## result,
## work,
## layer} => {train} 0.1000000 1.0000000 2.500000 3
## [19646] {network,
## train,
## work,
## layer} => {result} 0.1000000 1.0000000 3.000000 3
## [19647] {network,
## train,
## result,
## work} => {layer} 0.1000000 1.0000000 5.000000 3
## [19648] {represent,
## neural,
## work,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [19649] {network,
## neural,
## work,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [19650] {network,
## represent,
## neural,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [19651] {network,
## represent,
## work,
## layer} => {neural} 0.1000000 1.0000000 3.000000 3
## [19652] {network,
## represent,
## neural,
## work} => {layer} 0.1000000 1.0000000 5.000000 3
## [19653] {approach,
## complex,
## input,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [19654] {approach,
## complex,
## input,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [19655] {complex,
## input,
## model,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [19656] {approach,
## complex,
## model,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [19657] {approach,
## input,
## model,
## show} => {complex} 0.1000000 0.7500000 3.750000 3
## [19658] {approach,
## complex,
## input,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [19659] {approach,
## complex,
## featur,
## input} => {show} 0.1000000 1.0000000 1.875000 3
## [19660] {complex,
## featur,
## input,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [19661] {approach,
## complex,
## featur,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [19662] {approach,
## featur,
## input,
## show} => {complex} 0.1000000 1.0000000 5.000000 3
## [19663] {approach,
## complex,
## input,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [19664] {approach,
## complex,
## featur,
## input} => {model} 0.1000000 1.0000000 1.875000 3
## [19665] {complex,
## featur,
## input,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [19666] {approach,
## complex,
## featur,
## model} => {input} 0.1000000 1.0000000 4.285714 3
## [19667] {approach,
## featur,
## input,
## model} => {complex} 0.1000000 1.0000000 5.000000 3
## [19668] {complex,
## input,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [19669] {complex,
## featur,
## input,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [19670] {complex,
## featur,
## input,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [19671] {complex,
## featur,
## model,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [19672] {featur,
## input,
## model,
## show} => {complex} 0.1000000 1.0000000 5.000000 3
## [19673] {classif,
## complex,
## method,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [19674] {classif,
## complex,
## featur,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [19675] {classif,
## complex,
## featur,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [19676] {complex,
## featur,
## method,
## show} => {classif} 0.1000000 1.0000000 3.750000 3
## [19677] {classif,
## featur,
## method,
## show} => {complex} 0.1000000 0.7500000 3.750000 3
## [19678] {approach,
## complex,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [19679] {approach,
## complex,
## featur,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [19680] {approach,
## complex,
## featur,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [19681] {complex,
## featur,
## model,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [19682] {achiev,
## dataset,
## general,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [19683] {network,
## achiev,
## general,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19684] {network,
## achiev,
## dataset,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [19685] {network,
## dataset,
## general,
## result} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19686] {network,
## achiev,
## dataset,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [19687] {show,
## general,
## recognit,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [19688] {featur,
## general,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [19689] {featur,
## show,
## general,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [19690] {featur,
## show,
## general,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [19691] {featur,
## show,
## recognit,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [19692] {approach,
## dataset,
## general,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [19693] {approach,
## model,
## dataset,
## general} => {propos} 0.1000000 1.0000000 2.000000 3
## [19694] {approach,
## model,
## general,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19695] {model,
## dataset,
## general,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19696] {paper,
## task,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [19697] {paper,
## train,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [19698] {task,
## train,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19699] {paper,
## task,
## train,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19700] {paper,
## task,
## train,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19701] {paper,
## task,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19702] {paper,
## learn,
## signific,
## effect} => {task} 0.1000000 0.7500000 2.045455 3
## [19703] {task,
## learn,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19704] {paper,
## task,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19705] {paper,
## task,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19706] {paper,
## task,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [19707] {featur,
## paper,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [19708] {featur,
## task,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19709] {featur,
## paper,
## task,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19710] {featur,
## paper,
## task,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19711] {paper,
## train,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19712] {paper,
## learn,
## signific,
## effect} => {train} 0.1000000 0.7500000 1.875000 3
## [19713] {train,
## learn,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19714] {paper,
## train,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19715] {paper,
## train,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19716] {paper,
## train,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [19717] {featur,
## paper,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [19718] {featur,
## train,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19719] {featur,
## paper,
## train,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19720] {featur,
## paper,
## train,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19721] {paper,
## learn,
## signific,
## effect} => {represent} 0.1000000 0.7500000 1.500000 3
## [19722] {paper,
## represent,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19723] {represent,
## learn,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19724] {paper,
## represent,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19725] {paper,
## represent,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19726] {paper,
## learn,
## signific,
## effect} => {show} 0.1000000 0.7500000 1.406250 3
## [19727] {paper,
## show,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19728] {show,
## learn,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19729] {paper,
## show,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19730] {paper,
## show,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19731] {paper,
## learn,
## signific,
## effect} => {propos} 0.1000000 0.7500000 1.500000 3
## [19732] {paper,
## propos,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19733] {propos,
## learn,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19734] {paper,
## propos,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19735] {paper,
## propos,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19736] {paper,
## learn,
## signific,
## effect} => {featur} 0.1000000 0.7500000 1.406250 3
## [19737] {featur,
## paper,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19738] {featur,
## learn,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19739] {featur,
## paper,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19740] {featur,
## paper,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19741] {task,
## train,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19742] {task,
## learn,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [19743] {train,
## learn,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [19744] {task,
## train,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19745] {task,
## train,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19746] {task,
## train,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [19747] {featur,
## task,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [19748] {featur,
## train,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [19749] {featur,
## task,
## train,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19750] {featur,
## task,
## train,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19751] {task,
## learn,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [19752] {featur,
## task,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19753] {featur,
## learn,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [19754] {featur,
## task,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19755] {featur,
## task,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19756] {train,
## learn,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [19757] {featur,
## train,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19758] {featur,
## learn,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [19759] {featur,
## train,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [19760] {featur,
## train,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [19761] {method,
## learn,
## effect,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [19762] {method,
## represent,
## effect,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [19763] {represent,
## learn,
## effect,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [19764] {method,
## represent,
## learn,
## effect} => {success} 0.1000000 1.0000000 3.750000 3
## [19765] {method,
## represent,
## learn,
## success} => {effect} 0.1000000 0.7500000 3.214286 3
## [19766] {method,
## learn,
## effect,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [19767] {method,
## propos,
## effect,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [19768] {propos,
## learn,
## effect,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [19769] {method,
## propos,
## learn,
## effect} => {success} 0.1000000 1.0000000 3.750000 3
## [19770] {method,
## propos,
## learn,
## success} => {effect} 0.1000000 0.7500000 3.214286 3
## [19771] {method,
## represent,
## effect,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [19772] {method,
## propos,
## effect,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [19773] {represent,
## propos,
## effect,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [19774] {method,
## represent,
## propos,
## effect} => {success} 0.1000000 1.0000000 3.750000 3
## [19775] {method,
## represent,
## propos,
## success} => {effect} 0.1000000 0.7500000 3.214286 3
## [19776] {represent,
## learn,
## effect,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [19777] {propos,
## learn,
## effect,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [19778] {represent,
## propos,
## effect,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [19779] {represent,
## propos,
## learn,
## effect} => {success} 0.1000000 1.0000000 3.750000 3
## [19780] {represent,
## propos,
## learn,
## success} => {effect} 0.1000000 0.7500000 3.214286 3
## [19781] {classif,
## propos,
## learn,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [19782] {classif,
## featur,
## learn,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [19783] {classif,
## featur,
## propos,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19784] {featur,
## propos,
## learn,
## effect} => {classif} 0.1000000 1.0000000 3.750000 3
## [19785] {classif,
## featur,
## propos,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [19786] {perform,
## problem,
## learn,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [19787] {show,
## perform,
## problem,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19788] {show,
## problem,
## learn,
## effect} => {perform} 0.1000000 1.0000000 2.142857 3
## [19789] {show,
## perform,
## learn,
## effect} => {problem} 0.1000000 1.0000000 3.333333 3
## [19790] {show,
## perform,
## problem,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [19791] {perform,
## problem,
## learn,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [19792] {perform,
## propos,
## problem,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19793] {propos,
## problem,
## learn,
## effect} => {perform} 0.1000000 1.0000000 2.142857 3
## [19794] {perform,
## propos,
## learn,
## effect} => {problem} 0.1000000 1.0000000 3.333333 3
## [19795] {perform,
## propos,
## problem,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [19796] {show,
## perform,
## problem,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [19797] {perform,
## propos,
## problem,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [19798] {show,
## propos,
## problem,
## effect} => {perform} 0.1000000 1.0000000 2.142857 3
## [19799] {show,
## perform,
## propos,
## effect} => {problem} 0.1000000 1.0000000 3.333333 3
## [19800] {show,
## perform,
## propos,
## problem} => {effect} 0.1000000 0.7500000 3.214286 3
## [19801] {show,
## problem,
## learn,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [19802] {propos,
## problem,
## learn,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [19803] {show,
## propos,
## problem,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19804] {show,
## propos,
## learn,
## effect} => {problem} 0.1000000 1.0000000 3.333333 3
## [19805] {show,
## propos,
## problem,
## learn} => {effect} 0.1000000 1.0000000 4.285714 3
## [19806] {paper,
## task,
## train,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19807] {paper,
## task,
## learn,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [19808] {paper,
## train,
## learn,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [19809] {task,
## train,
## learn,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19810] {paper,
## task,
## train,
## learn} => {effect} 0.1000000 1.0000000 4.285714 3
## [19811] {paper,
## task,
## train,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [19812] {featur,
## paper,
## task,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [19813] {featur,
## paper,
## train,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [19814] {featur,
## task,
## train,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19815] {featur,
## paper,
## task,
## train} => {effect} 0.1000000 0.7500000 3.214286 3
## [19816] {paper,
## task,
## learn,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [19817] {featur,
## paper,
## task,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19818] {featur,
## paper,
## learn,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [19819] {featur,
## task,
## learn,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19820] {featur,
## paper,
## task,
## learn} => {effect} 0.1000000 1.0000000 4.285714 3
## [19821] {paper,
## train,
## learn,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [19822] {featur,
## paper,
## train,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19823] {featur,
## paper,
## learn,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [19824] {featur,
## train,
## learn,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [19825] {featur,
## paper,
## train,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [19826] {method,
## represent,
## learn,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [19827] {method,
## propos,
## learn,
## effect} => {represent} 0.1000000 1.0000000 2.000000 3
## [19828] {method,
## represent,
## propos,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19829] {represent,
## propos,
## learn,
## effect} => {method} 0.1000000 1.0000000 2.727273 3
## [19830] {method,
## represent,
## propos,
## learn} => {effect} 0.1000000 0.7500000 3.214286 3
## [19831] {task,
## train,
## learn,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [19832] {featur,
## task,
## train,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19833] {featur,
## task,
## learn,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [19834] {featur,
## train,
## learn,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [19835] {featur,
## task,
## train,
## learn} => {effect} 0.1000000 1.0000000 4.285714 3
## [19836] {show,
## perform,
## learn,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [19837] {perform,
## propos,
## learn,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [19838] {show,
## perform,
## propos,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [19839] {show,
## propos,
## learn,
## effect} => {perform} 0.1000000 1.0000000 2.142857 3
## [19840] {show,
## perform,
## propos,
## learn} => {effect} 0.1000000 1.0000000 4.285714 3
## [19841] {perform,
## problem,
## applic,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [19842] {represent,
## problem,
## applic,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [19843] {represent,
## perform,
## applic,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [19844] {represent,
## perform,
## problem,
## applic} => {success} 0.1000000 0.7500000 2.812500 3
## [19845] {represent,
## perform,
## problem,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [19846] {perform,
## problem,
## applic,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [19847] {propos,
## problem,
## applic,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [19848] {perform,
## propos,
## applic,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [19849] {perform,
## propos,
## problem,
## applic} => {success} 0.1000000 0.7500000 2.812500 3
## [19850] {perform,
## propos,
## problem,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [19851] {represent,
## problem,
## applic,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [19852] {propos,
## problem,
## applic,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [19853] {represent,
## propos,
## applic,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [19854] {represent,
## propos,
## problem,
## applic} => {success} 0.1000000 0.7500000 2.812500 3
## [19855] {represent,
## propos,
## problem,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [19856] {represent,
## perform,
## applic,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [19857] {perform,
## propos,
## applic,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [19858] {represent,
## propos,
## applic,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [19859] {represent,
## perform,
## propos,
## applic} => {success} 0.1000000 0.7500000 2.812500 3
## [19860] {represent,
## perform,
## propos,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [19861] {approach,
## make,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19862] {make,
## perform,
## problem,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [19863] {approach,
## make,
## perform,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19864] {approach,
## perform,
## problem,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [19865] {approach,
## make,
## perform,
## problem} => {applic} 0.1000000 0.7500000 3.214286 3
## [19866] {approach,
## make,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19867] {make,
## represent,
## problem,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [19868] {approach,
## make,
## represent,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19869] {approach,
## represent,
## problem,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [19870] {approach,
## make,
## represent,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [19871] {approach,
## make,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19872] {make,
## propos,
## problem,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [19873] {approach,
## make,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19874] {approach,
## propos,
## problem,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [19875] {approach,
## make,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [19876] {make,
## perform,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19877] {make,
## represent,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19878] {make,
## represent,
## perform,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19879] {represent,
## perform,
## problem,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [19880] {make,
## represent,
## perform,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [19881] {make,
## perform,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19882] {make,
## propos,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19883] {make,
## perform,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19884] {perform,
## propos,
## problem,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [19885] {make,
## perform,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [19886] {make,
## represent,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19887] {make,
## propos,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19888] {make,
## represent,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19889] {represent,
## propos,
## problem,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [19890] {make,
## represent,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [19891] {approach,
## make,
## perform,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19892] {approach,
## make,
## represent,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19893] {make,
## represent,
## perform,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [19894] {approach,
## represent,
## perform,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [19895] {approach,
## make,
## represent,
## perform} => {applic} 0.1000000 1.0000000 4.285714 3
## [19896] {approach,
## make,
## perform,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19897] {approach,
## make,
## propos,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19898] {make,
## perform,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [19899] {approach,
## perform,
## propos,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [19900] {approach,
## make,
## perform,
## propos} => {applic} 0.1000000 1.0000000 4.285714 3
## [19901] {approach,
## make,
## represent,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19902] {approach,
## make,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19903] {make,
## represent,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [19904] {approach,
## represent,
## propos,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [19905] {approach,
## make,
## represent,
## propos} => {applic} 0.1000000 0.7500000 3.214286 3
## [19906] {make,
## represent,
## perform,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19907] {make,
## perform,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19908] {make,
## represent,
## propos,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19909] {represent,
## perform,
## propos,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [19910] {make,
## represent,
## perform,
## propos} => {applic} 0.1000000 0.7500000 3.214286 3
## [19911] {approach,
## perform,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19912] {approach,
## represent,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19913] {represent,
## perform,
## problem,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [19914] {approach,
## represent,
## perform,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19915] {approach,
## represent,
## perform,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [19916] {approach,
## perform,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19917] {approach,
## propos,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19918] {perform,
## propos,
## problem,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [19919] {approach,
## perform,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19920] {approach,
## perform,
## propos,
## problem} => {applic} 0.1000000 0.7500000 3.214286 3
## [19921] {approach,
## represent,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19922] {approach,
## propos,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19923] {represent,
## propos,
## problem,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [19924] {approach,
## represent,
## propos,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [19925] {approach,
## represent,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [19926] {perform,
## problem,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19927] {represent,
## perform,
## problem,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [19928] {represent,
## problem,
## learn,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19929] {represent,
## perform,
## learn,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19930] {represent,
## perform,
## problem,
## learn} => {applic} 0.1000000 1.0000000 4.285714 3
## [19931] {perform,
## problem,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19932] {perform,
## propos,
## problem,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [19933] {propos,
## problem,
## learn,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19934] {perform,
## propos,
## learn,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19935] {perform,
## propos,
## problem,
## learn} => {applic} 0.1000000 0.7500000 3.214286 3
## [19936] {represent,
## perform,
## problem,
## applic} => {propos} 0.1333333 1.0000000 2.000000 4
## [19937] {perform,
## propos,
## problem,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [19938] {represent,
## propos,
## problem,
## applic} => {perform} 0.1333333 1.0000000 2.142857 4
## [19939] {represent,
## perform,
## propos,
## applic} => {problem} 0.1333333 1.0000000 3.333333 4
## [19940] {represent,
## perform,
## propos,
## problem} => {applic} 0.1333333 1.0000000 4.285714 4
## [19941] {represent,
## perform,
## problem,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [19942] {featur,
## perform,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19943] {featur,
## represent,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19944] {featur,
## represent,
## perform,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [19945] {featur,
## represent,
## perform,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [19946] {perform,
## propos,
## problem,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [19947] {featur,
## perform,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19948] {featur,
## propos,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [19949] {featur,
## perform,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19950] {featur,
## perform,
## propos,
## problem} => {applic} 0.1000000 0.7500000 3.214286 3
## [19951] {represent,
## problem,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19952] {propos,
## problem,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19953] {represent,
## propos,
## problem,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [19954] {represent,
## propos,
## learn,
## applic} => {problem} 0.1000000 0.7500000 2.500000 3
## [19955] {represent,
## propos,
## problem,
## learn} => {applic} 0.1000000 1.0000000 4.285714 3
## [19956] {represent,
## propos,
## problem,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [19957] {featur,
## represent,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19958] {featur,
## propos,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19959] {featur,
## represent,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [19960] {featur,
## represent,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [19961] {data,
## result,
## work,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19962] {dataset,
## result,
## work,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [19963] {data,
## dataset,
## result,
## applic} => {work} 0.1000000 1.0000000 2.500000 3
## [19964] {data,
## dataset,
## work,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [19965] {data,
## dataset,
## result,
## work} => {applic} 0.1000000 1.0000000 4.285714 3
## [19966] {data,
## result,
## work,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19967] {represent,
## result,
## work,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [19968] {data,
## represent,
## result,
## applic} => {work} 0.1000000 1.0000000 2.500000 3
## [19969] {data,
## represent,
## work,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [19970] {data,
## represent,
## result,
## work} => {applic} 0.1000000 1.0000000 4.285714 3
## [19971] {dataset,
## result,
## work,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19972] {represent,
## result,
## work,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19973] {represent,
## dataset,
## result,
## applic} => {work} 0.1000000 1.0000000 2.500000 3
## [19974] {represent,
## dataset,
## work,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [19975] {represent,
## dataset,
## result,
## work} => {applic} 0.1000000 1.0000000 4.285714 3
## [19976] {data,
## dataset,
## result,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19977] {data,
## represent,
## result,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19978] {represent,
## dataset,
## result,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [19979] {data,
## represent,
## dataset,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [19980] {data,
## represent,
## dataset,
## result} => {applic} 0.1000000 0.7500000 3.214286 3
## [19981] {approach,
## data,
## task,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19982] {approach,
## represent,
## task,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [19983] {data,
## represent,
## task,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [19984] {approach,
## data,
## represent,
## applic} => {task} 0.1000000 1.0000000 2.727273 3
## [19985] {approach,
## data,
## task,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19986] {approach,
## task,
## propos,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [19987] {data,
## task,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [19988] {approach,
## data,
## propos,
## applic} => {task} 0.1000000 1.0000000 2.727273 3
## [19989] {approach,
## data,
## task,
## propos} => {applic} 0.1000000 0.7500000 3.214286 3
## [19990] {approach,
## represent,
## task,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19991] {approach,
## task,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19992] {represent,
## task,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [19993] {approach,
## represent,
## propos,
## applic} => {task} 0.1000000 0.7500000 2.045455 3
## [19994] {data,
## represent,
## task,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [19995] {data,
## task,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [19996] {represent,
## task,
## propos,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [19997] {data,
## represent,
## propos,
## applic} => {task} 0.1000000 1.0000000 2.727273 3
## [19998] {data,
## represent,
## task,
## propos} => {applic} 0.1000000 0.7500000 3.214286 3
## [19999] {approach,
## represent,
## perform,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [20000] {approach,
## perform,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [20001] {approach,
## represent,
## propos,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [20002] {represent,
## perform,
## propos,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [20003] {approach,
## represent,
## perform,
## propos} => {applic} 0.1000000 1.0000000 4.285714 3
## [20004] {approach,
## data,
## represent,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [20005] {approach,
## data,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [20006] {approach,
## represent,
## propos,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [20007] {data,
## represent,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [20008] {approach,
## data,
## represent,
## propos} => {applic} 0.1000000 0.7500000 3.214286 3
## [20009] {approach,
## dataset,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [20010] {approach,
## represent,
## dataset,
## applic} => {learn} 0.1000000 1.0000000 2.307692 3
## [20011] {approach,
## represent,
## learn,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20012] {represent,
## dataset,
## learn,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [20013] {approach,
## represent,
## dataset,
## learn} => {applic} 0.1000000 0.7500000 3.214286 3
## [20014] {approach,
## dataset,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [20015] {approach,
## dataset,
## propos,
## applic} => {learn} 0.1000000 1.0000000 2.307692 3
## [20016] {approach,
## propos,
## learn,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20017] {dataset,
## propos,
## learn,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [20018] {approach,
## dataset,
## propos,
## learn} => {applic} 0.1000000 0.7500000 3.214286 3
## [20019] {approach,
## represent,
## dataset,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [20020] {approach,
## dataset,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [20021] {approach,
## represent,
## propos,
## applic} => {dataset} 0.1000000 0.7500000 1.730769 3
## [20022] {represent,
## dataset,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [20023] {approach,
## represent,
## dataset,
## propos} => {applic} 0.1000000 0.7500000 3.214286 3
## [20024] {approach,
## represent,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [20025] {approach,
## propos,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [20026] {approach,
## represent,
## propos,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [20027] {represent,
## propos,
## learn,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [20028] {data,
## dataset,
## work,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [20029] {data,
## represent,
## work,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20030] {represent,
## dataset,
## work,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [20031] {data,
## represent,
## dataset,
## applic} => {work} 0.1000000 1.0000000 2.500000 3
## [20032] {data,
## represent,
## dataset,
## work} => {applic} 0.1000000 0.7500000 3.214286 3
## [20033] {data,
## represent,
## perform,
## applic} => {featur} 0.1000000 1.0000000 1.875000 3
## [20034] {data,
## featur,
## perform,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [20035] {featur,
## represent,
## perform,
## applic} => {data} 0.1000000 0.7500000 1.730769 3
## [20036] {data,
## featur,
## represent,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [20037] {data,
## featur,
## represent,
## perform} => {applic} 0.1000000 0.7500000 3.214286 3
## [20038] {represent,
## perform,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [20039] {perform,
## propos,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [20040] {represent,
## perform,
## propos,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [20041] {represent,
## propos,
## learn,
## applic} => {perform} 0.1000000 0.7500000 1.607143 3
## [20042] {represent,
## perform,
## propos,
## learn} => {applic} 0.1000000 0.7500000 3.214286 3
## [20043] {represent,
## perform,
## propos,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [20044] {featur,
## represent,
## perform,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [20045] {featur,
## perform,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [20046] {featur,
## represent,
## propos,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [20047] {featur,
## represent,
## perform,
## propos} => {applic} 0.1000000 0.7500000 3.214286 3
## [20048] {represent,
## dataset,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [20049] {dataset,
## propos,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [20050] {represent,
## dataset,
## propos,
## applic} => {learn} 0.1000000 1.0000000 2.307692 3
## [20051] {represent,
## propos,
## learn,
## applic} => {dataset} 0.1000000 0.7500000 1.730769 3
## [20052] {represent,
## show,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [20053] {represent,
## propos,
## learn,
## applic} => {show} 0.1000000 0.7500000 1.406250 3
## [20054] {show,
## propos,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [20055] {represent,
## show,
## propos,
## applic} => {learn} 0.1000000 1.0000000 2.307692 3
## [20056] {represent,
## show,
## propos,
## learn} => {applic} 0.1000000 0.7500000 3.214286 3
## [20057] {approach,
## input,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20058] {approach,
## input,
## show,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20059] {input,
## show,
## learn,
## recent} => {approach} 0.1000000 1.0000000 2.500000 3
## [20060] {approach,
## input,
## show,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [20061] {approach,
## show,
## learn,
## recent} => {input} 0.1000000 1.0000000 4.285714 3
## [20062] {approach,
## input,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20063] {approach,
## input,
## model,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20064] {input,
## model,
## learn,
## recent} => {approach} 0.1000000 1.0000000 2.500000 3
## [20065] {approach,
## input,
## model,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [20066] {approach,
## model,
## learn,
## recent} => {input} 0.1000000 1.0000000 4.285714 3
## [20067] {approach,
## input,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20068] {approach,
## input,
## model,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20069] {input,
## model,
## show,
## recent} => {approach} 0.1000000 1.0000000 2.500000 3
## [20070] {approach,
## input,
## model,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [20071] {approach,
## model,
## show,
## recent} => {input} 0.1000000 1.0000000 4.285714 3
## [20072] {input,
## show,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20073] {input,
## model,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20074] {input,
## model,
## show,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20075] {input,
## model,
## show,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [20076] {approach,
## input,
## make,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [20077] {input,
## make,
## perform,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [20078] {approach,
## input,
## make,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [20079] {approach,
## input,
## perform,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [20080] {approach,
## make,
## perform,
## problem} => {input} 0.1000000 0.7500000 3.214286 3
## [20081] {approach,
## input,
## make,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [20082] {input,
## make,
## method,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [20083] {approach,
## input,
## make,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [20084] {approach,
## input,
## method,
## show} => {make} 0.1000000 1.0000000 3.333333 3
## [20085] {approach,
## make,
## method,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [20086] {approach,
## input,
## make,
## method} => {model} 0.1000000 1.0000000 1.875000 3
## [20087] {input,
## make,
## method,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [20088] {approach,
## input,
## make,
## model} => {method} 0.1000000 1.0000000 2.727273 3
## [20089] {approach,
## input,
## method,
## model} => {make} 0.1000000 1.0000000 3.333333 3
## [20090] {approach,
## make,
## method,
## model} => {input} 0.1000000 1.0000000 4.285714 3
## [20091] {input,
## make,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [20092] {input,
## make,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [20093] {input,
## make,
## model,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [20094] {input,
## method,
## model,
## show} => {make} 0.1000000 1.0000000 3.333333 3
## [20095] {make,
## method,
## model,
## show} => {input} 0.1000000 0.7500000 3.214286 3
## [20096] {approach,
## input,
## make,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [20097] {approach,
## input,
## make,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [20098] {input,
## make,
## model,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [20099] {approach,
## input,
## model,
## show} => {make} 0.1000000 0.7500000 2.500000 3
## [20100] {approach,
## make,
## model,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [20101] {input,
## paper,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [20102] {input,
## model,
## paper,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [20103] {input,
## model,
## paper,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [20104] {input,
## model,
## represent,
## show} => {paper} 0.1000000 0.7500000 2.250000 3
## [20105] {model,
## paper,
## represent,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [20106] {approach,
## input,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [20107] {approach,
## input,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [20108] {input,
## method,
## model,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [20109] {approach,
## input,
## model,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [20110] {approach,
## input,
## show,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [20111] {approach,
## input,
## model,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [20112] {approach,
## input,
## model,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [20113] {input,
## model,
## show,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [20114] {approach,
## model,
## show,
## learn} => {input} 0.1000000 0.7500000 3.214286 3
## [20115] {approach,
## input,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [20116] {approach,
## input,
## model,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [20117] {approach,
## input,
## model,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [20118] {input,
## model,
## represent,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [20119] {approach,
## model,
## represent,
## show} => {input} 0.1000000 0.7500000 3.214286 3
## [20120] {approach,
## input,
## model,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [20121] {approach,
## featur,
## input,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [20122] {approach,
## featur,
## input,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [20123] {featur,
## input,
## model,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [20124] {input,
## model,
## represent,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [20125] {input,
## network,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [20126] {input,
## model,
## network,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [20127] {input,
## model,
## network,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [20128] {model,
## network,
## represent,
## show} => {input} 0.1000000 0.7500000 3.214286 3
## [20129] {reduc,
## comput,
## optim,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [20130] {reduc,
## improv,
## comput,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [20131] {reduc,
## improv,
## comput,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [20132] {improv,
## comput,
## optim,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [20133] {reduc,
## improv,
## optim,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [20134] {machin,
## show,
## problem,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20135] {machin,
## model,
## problem,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20136] {machin,
## model,
## show,
## recent} => {problem} 0.1000000 0.7500000 2.500000 3
## [20137] {model,
## show,
## problem,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [20138] {machin,
## model,
## show,
## problem} => {recent} 0.1000000 1.0000000 4.285714 3
## [20139] {machin,
## show,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20140] {machin,
## model,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20141] {machin,
## model,
## show,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [20142] {model,
## show,
## algorithm,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [20143] {machin,
## model,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [20144] {machin,
## show,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20145] {machin,
## model,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20146] {machin,
## model,
## show,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [20147] {machin,
## model,
## show,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [20148] {machin,
## show,
## learn,
## recent} => {featur} 0.1000000 1.0000000 1.875000 3
## [20149] {featur,
## machin,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20150] {featur,
## machin,
## show,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20151] {featur,
## show,
## learn,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [20152] {featur,
## machin,
## show,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [20153] {machin,
## model,
## learn,
## recent} => {featur} 0.1000000 1.0000000 1.875000 3
## [20154] {featur,
## machin,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20155] {featur,
## machin,
## model,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20156] {featur,
## model,
## learn,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [20157] {featur,
## machin,
## model,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [20158] {machin,
## model,
## show,
## recent} => {featur} 0.1000000 0.7500000 1.406250 3
## [20159] {featur,
## machin,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20160] {featur,
## machin,
## model,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20161] {featur,
## model,
## show,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [20162] {featur,
## machin,
## model,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [20163] {show,
## problem,
## recent,
## signific} => {model} 0.1000000 1.0000000 1.875000 3
## [20164] {model,
## problem,
## recent,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [20165] {model,
## show,
## recent,
## signific} => {problem} 0.1000000 1.0000000 3.333333 3
## [20166] {model,
## show,
## problem,
## recent} => {signific} 0.1000000 0.7500000 2.812500 3
## [20167] {model,
## show,
## problem,
## signific} => {recent} 0.1000000 1.0000000 4.285714 3
## [20168] {method,
## show,
## problem,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20169] {method,
## model,
## problem,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20170] {model,
## show,
## problem,
## recent} => {method} 0.1000000 0.7500000 2.045455 3
## [20171] {method,
## model,
## show,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [20172] {method,
## model,
## show,
## problem} => {recent} 0.1000000 1.0000000 4.285714 3
## [20173] {perform,
## problem,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20174] {show,
## perform,
## problem,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20175] {show,
## problem,
## learn,
## recent} => {perform} 0.1000000 1.0000000 2.142857 3
## [20176] {show,
## perform,
## learn,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [20177] {show,
## perform,
## problem,
## learn} => {recent} 0.1000000 0.7500000 3.214286 3
## [20178] {perform,
## problem,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20179] {model,
## perform,
## problem,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20180] {model,
## problem,
## learn,
## recent} => {perform} 0.1000000 1.0000000 2.142857 3
## [20181] {model,
## perform,
## learn,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [20182] {model,
## perform,
## problem,
## learn} => {recent} 0.1000000 0.7500000 3.214286 3
## [20183] {show,
## perform,
## problem,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20184] {model,
## perform,
## problem,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20185] {model,
## show,
## problem,
## recent} => {perform} 0.1000000 0.7500000 1.607143 3
## [20186] {model,
## show,
## perform,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [20187] {model,
## show,
## perform,
## problem} => {recent} 0.1000000 1.0000000 4.285714 3
## [20188] {show,
## problem,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20189] {model,
## problem,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20190] {model,
## show,
## problem,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [20191] {model,
## show,
## problem,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [20192] {show,
## algorithm,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20193] {model,
## algorithm,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20194] {model,
## show,
## algorithm,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [20195] {model,
## show,
## algorithm,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [20196] {show,
## algorithm,
## learn,
## recent} => {featur} 0.1000000 1.0000000 1.875000 3
## [20197] {featur,
## algorithm,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20198] {featur,
## show,
## algorithm,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20199] {featur,
## show,
## learn,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [20200] {featur,
## show,
## algorithm,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [20201] {model,
## algorithm,
## learn,
## recent} => {featur} 0.1000000 1.0000000 1.875000 3
## [20202] {featur,
## algorithm,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20203] {featur,
## model,
## algorithm,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20204] {featur,
## model,
## learn,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [20205] {featur,
## model,
## algorithm,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [20206] {model,
## show,
## algorithm,
## recent} => {featur} 0.1000000 0.7500000 1.406250 3
## [20207] {featur,
## show,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20208] {featur,
## model,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20209] {featur,
## model,
## show,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [20210] {featur,
## model,
## show,
## algorithm} => {recent} 0.1000000 0.7500000 3.214286 3
## [20211] {approach,
## show,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20212] {approach,
## model,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20213] {approach,
## model,
## show,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20214] {approach,
## model,
## show,
## learn} => {recent} 0.1000000 0.7500000 3.214286 3
## [20215] {show,
## perform,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20216] {model,
## perform,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20217] {model,
## show,
## perform,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20218] {model,
## show,
## perform,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [20219] {represent,
## show,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [20220] {model,
## represent,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [20221] {model,
## represent,
## show,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [20222] {model,
## represent,
## show,
## learn} => {recent} 0.1000000 0.7500000 3.214286 3
## [20223] {model,
## show,
## learn,
## recent} => {featur} 0.1333333 0.8000000 1.500000 4
## [20224] {featur,
## show,
## learn,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [20225] {featur,
## model,
## learn,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [20226] {featur,
## model,
## show,
## recent} => {learn} 0.1333333 1.0000000 2.307692 4
## [20227] {featur,
## model,
## show,
## learn} => {recent} 0.1333333 0.8000000 3.428571 4
## [20228] {classif,
## machin,
## make,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [20229] {approach,
## classif,
## machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [20230] {approach,
## machin,
## make,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [20231] {approach,
## classif,
## machin,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [20232] {approach,
## classif,
## make,
## method} => {machin} 0.1000000 1.0000000 4.285714 3
## [20233] {classif,
## machin,
## make,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [20234] {classif,
## featur,
## machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [20235] {featur,
## machin,
## make,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [20236] {classif,
## featur,
## machin,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [20237] {classif,
## featur,
## make,
## method} => {machin} 0.1000000 1.0000000 4.285714 3
## [20238] {approach,
## classif,
## machin,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [20239] {classif,
## featur,
## machin,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [20240] {approach,
## featur,
## machin,
## make} => {classif} 0.1000000 1.0000000 3.750000 3
## [20241] {approach,
## classif,
## featur,
## machin} => {make} 0.1000000 1.0000000 3.333333 3
## [20242] {approach,
## classif,
## featur,
## make} => {machin} 0.1000000 1.0000000 4.285714 3
## [20243] {approach,
## machin,
## make,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [20244] {featur,
## machin,
## make,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [20245] {approach,
## featur,
## machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [20246] {approach,
## featur,
## machin,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [20247] {approach,
## featur,
## make,
## method} => {machin} 0.1000000 1.0000000 4.285714 3
## [20248] {classif,
## machin,
## paper,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [20249] {classif,
## machin,
## paper,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [20250] {classif,
## machin,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [20251] {machin,
## paper,
## task,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [20252] {classif,
## paper,
## task,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [20253] {classif,
## machin,
## paper,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20254] {classif,
## featur,
## machin,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [20255] {classif,
## featur,
## machin,
## task} => {paper} 0.1000000 1.0000000 3.000000 3
## [20256] {featur,
## machin,
## paper,
## task} => {classif} 0.1000000 1.0000000 3.750000 3
## [20257] {classif,
## featur,
## paper,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [20258] {classif,
## machin,
## paper,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [20259] {classif,
## featur,
## machin,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [20260] {classif,
## featur,
## machin,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [20261] {featur,
## machin,
## paper,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [20262] {classif,
## featur,
## paper,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [20263] {approach,
## classif,
## machin,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [20264] {classif,
## featur,
## machin,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [20265] {approach,
## classif,
## featur,
## machin} => {method} 0.1000000 1.0000000 2.727273 3
## [20266] {approach,
## featur,
## machin,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [20267] {approach,
## classif,
## featur,
## method} => {machin} 0.1000000 0.7500000 3.214286 3
## [20268] {classif,
## machin,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [20269] {classif,
## featur,
## machin,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [20270] {classif,
## featur,
## machin,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [20271] {featur,
## machin,
## task,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [20272] {classif,
## featur,
## task,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [20273] {classif,
## machin,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [20274] {classif,
## featur,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [20275] {classif,
## featur,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [20276] {featur,
## machin,
## model,
## show} => {classif} 0.1000000 0.7500000 2.812500 3
## [20277] {classif,
## featur,
## model,
## show} => {machin} 0.1000000 0.7500000 3.214286 3
## [20278] {machin,
## paper,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [20279] {featur,
## machin,
## paper,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [20280] {featur,
## machin,
## paper,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [20281] {featur,
## machin,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [20282] {featur,
## paper,
## task,
## train} => {machin} 0.1000000 0.7500000 3.214286 3
## [20283] {data,
## machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [20284] {data,
## machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20285] {machin,
## model,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20286] {data,
## machin,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20287] {data,
## model,
## show,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [20288] {data,
## machin,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20289] {data,
## featur,
## machin,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20290] {featur,
## machin,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20291] {data,
## featur,
## machin,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20292] {data,
## machin,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20293] {data,
## featur,
## machin,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [20294] {featur,
## machin,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20295] {data,
## featur,
## machin,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [20296] {machin,
## model,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20297] {featur,
## machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [20298] {featur,
## machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20299] {featur,
## machin,
## model,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [20300] {featur,
## model,
## show,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [20301] {data,
## machin,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [20302] {data,
## featur,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [20303] {data,
## featur,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [20304] {featur,
## machin,
## model,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [20305] {data,
## featur,
## model,
## show} => {machin} 0.1000000 0.7500000 3.214286 3
## [20306] {machin,
## model,
## show,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20307] {featur,
## machin,
## show,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [20308] {featur,
## machin,
## model,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [20309] {featur,
## machin,
## model,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [20310] {classif,
## architectur,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20311] {architectur,
## dataset,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [20312] {classif,
## dataset,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20313] {classif,
## architectur,
## dataset,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [20314] {classif,
## architectur,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [20315] {classif,
## architectur,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20316] {architectur,
## experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20317] {classif,
## experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20318] {classif,
## architectur,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20319] {classif,
## architectur,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [20320] {classif,
## architectur,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20321] {network,
## architectur,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [20322] {classif,
## network,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20323] {classif,
## network,
## architectur,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [20324] {classif,
## network,
## architectur,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [20325] {architectur,
## dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20326] {architectur,
## experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20327] {dataset,
## experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20328] {architectur,
## dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20329] {architectur,
## dataset,
## experi,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [20330] {architectur,
## dataset,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20331] {network,
## architectur,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20332] {network,
## dataset,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20333] {network,
## architectur,
## dataset,
## process} => {experi} 0.1000000 0.7500000 2.812500 3
## [20334] {network,
## architectur,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [20335] {architectur,
## experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20336] {network,
## architectur,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20337] {network,
## experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20338] {network,
## architectur,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20339] {network,
## architectur,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [20340] {classif,
## dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20341] {classif,
## experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20342] {dataset,
## experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20343] {classif,
## dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20344] {classif,
## dataset,
## experi,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [20345] {classif,
## dataset,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20346] {classif,
## network,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20347] {network,
## dataset,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [20348] {classif,
## network,
## dataset,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [20349] {classif,
## network,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [20350] {classif,
## experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20351] {classif,
## network,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20352] {network,
## experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20353] {classif,
## network,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20354] {classif,
## network,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [20355] {dataset,
## experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20356] {network,
## dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20357] {network,
## experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20358] {network,
## dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20359] {network,
## dataset,
## experi,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [20360] {classif,
## architectur,
## dataset,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20361] {classif,
## architectur,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20362] {architectur,
## dataset,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20363] {classif,
## dataset,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20364] {classif,
## architectur,
## dataset,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [20365] {classif,
## architectur,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20366] {classif,
## network,
## architectur,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20367] {network,
## architectur,
## dataset,
## process} => {classif} 0.1000000 0.7500000 2.812500 3
## [20368] {classif,
## network,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20369] {classif,
## network,
## architectur,
## dataset} => {process} 0.1000000 1.0000000 5.000000 3
## [20370] {classif,
## architectur,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20371] {classif,
## network,
## architectur,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20372] {network,
## architectur,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20373] {classif,
## network,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20374] {classif,
## network,
## architectur,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [20375] {model,
## architectur,
## process,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [20376] {featur,
## architectur,
## process,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [20377] {featur,
## model,
## architectur,
## process} => {recognit} 0.1000000 1.0000000 3.333333 3
## [20378] {featur,
## model,
## process,
## recognit} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20379] {featur,
## model,
## architectur,
## recognit} => {process} 0.1000000 1.0000000 5.000000 3
## [20380] {architectur,
## improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20381] {algorithm,
## architectur,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20382] {algorithm,
## architectur,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20383] {algorithm,
## improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20384] {algorithm,
## architectur,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20385] {architectur,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20386] {architectur,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20387] {architectur,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20388] {improv,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20389] {architectur,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20390] {architectur,
## improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20391] {architectur,
## dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20392] {architectur,
## dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20393] {dataset,
## improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20394] {architectur,
## dataset,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20395] {architectur,
## improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20396] {network,
## architectur,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20397] {network,
## architectur,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20398] {network,
## improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20399] {network,
## architectur,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20400] {algorithm,
## architectur,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20401] {architectur,
## improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20402] {algorithm,
## architectur,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20403] {algorithm,
## improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20404] {algorithm,
## architectur,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20405] {algorithm,
## architectur,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20406] {architectur,
## dataset,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20407] {algorithm,
## architectur,
## dataset,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20408] {algorithm,
## dataset,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20409] {algorithm,
## architectur,
## dataset,
## improv} => {process} 0.1000000 1.0000000 5.000000 3
## [20410] {algorithm,
## architectur,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20411] {network,
## architectur,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20412] {network,
## algorithm,
## architectur,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20413] {network,
## algorithm,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20414] {network,
## algorithm,
## architectur,
## improv} => {process} 0.1000000 1.0000000 5.000000 3
## [20415] {architectur,
## improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20416] {architectur,
## dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20417] {architectur,
## dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20418] {dataset,
## improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20419] {architectur,
## dataset,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20420] {architectur,
## improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20421] {network,
## architectur,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20422] {network,
## architectur,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20423] {network,
## improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20424] {network,
## architectur,
## improv,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [20425] {architectur,
## dataset,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20426] {network,
## architectur,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20427] {network,
## architectur,
## dataset,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [20428] {network,
## dataset,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20429] {network,
## architectur,
## dataset,
## improv} => {process} 0.1000000 1.0000000 5.000000 3
## [20430] {algorithm,
## architectur,
## process,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [20431] {featur,
## architectur,
## process,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20432] {featur,
## algorithm,
## architectur,
## process} => {result} 0.1000000 1.0000000 3.000000 3
## [20433] {featur,
## algorithm,
## process,
## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20434] {featur,
## algorithm,
## architectur,
## result} => {process} 0.1000000 1.0000000 5.000000 3
## [20435] {algorithm,
## architectur,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20436] {architectur,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20437] {algorithm,
## architectur,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20438] {algorithm,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20439] {algorithm,
## architectur,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20440] {algorithm,
## architectur,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20441] {architectur,
## dataset,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20442] {algorithm,
## architectur,
## dataset,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20443] {algorithm,
## dataset,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20444] {algorithm,
## architectur,
## dataset,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20445] {algorithm,
## architectur,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20446] {network,
## architectur,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20447] {network,
## algorithm,
## architectur,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20448] {network,
## algorithm,
## neural,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [20449] {network,
## algorithm,
## architectur,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20450] {architectur,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20451] {architectur,
## dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20452] {architectur,
## dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20453] {dataset,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20454] {architectur,
## dataset,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20455] {architectur,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20456] {network,
## architectur,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20457] {network,
## architectur,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20458] {network,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20459] {network,
## architectur,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20460] {architectur,
## dataset,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20461] {network,
## architectur,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20462] {network,
## architectur,
## dataset,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [20463] {network,
## dataset,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20464] {network,
## architectur,
## dataset,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20465] {algorithm,
## architectur,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20466] {algorithm,
## architectur,
## dataset,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20467] {architectur,
## dataset,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20468] {algorithm,
## dataset,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20469] {algorithm,
## architectur,
## dataset,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20470] {algorithm,
## architectur,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20471] {network,
## algorithm,
## architectur,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20472] {network,
## architectur,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20473] {network,
## algorithm,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20474] {network,
## algorithm,
## architectur,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20475] {algorithm,
## architectur,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20476] {network,
## algorithm,
## architectur,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20477] {network,
## architectur,
## dataset,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [20478] {network,
## algorithm,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20479] {network,
## algorithm,
## architectur,
## dataset} => {process} 0.1000000 1.0000000 5.000000 3
## [20480] {architectur,
## dataset,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [20481] {network,
## architectur,
## process,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20482] {network,
## architectur,
## dataset,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [20483] {network,
## dataset,
## process,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20484] {network,
## architectur,
## dataset,
## work} => {process} 0.1000000 0.7500000 3.750000 3
## [20485] {architectur,
## dataset,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20486] {network,
## architectur,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20487] {network,
## architectur,
## dataset,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [20488] {network,
## dataset,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20489] {network,
## architectur,
## dataset,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [20490] {architectur,
## dataset,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20491] {network,
## architectur,
## dataset,
## process} => {propos} 0.1000000 0.7500000 1.500000 3
## [20492] {network,
## architectur,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20493] {network,
## dataset,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20494] {network,
## architectur,
## dataset,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [20495] {featur,
## architectur,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20496] {network,
## architectur,
## dataset,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [20497] {featur,
## network,
## architectur,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20498] {featur,
## network,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20499] {featur,
## network,
## architectur,
## dataset} => {process} 0.1000000 0.7500000 3.750000 3
## [20500] {classif,
## dataset,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20501] {classif,
## network,
## dataset,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20502] {classif,
## network,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20503] {network,
## dataset,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20504] {classif,
## network,
## dataset,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [20505] {algorithm,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20506] {improv,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20507] {algorithm,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20508] {algorithm,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20509] {algorithm,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20510] {algorithm,
## improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20511] {dataset,
## improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20512] {algorithm,
## dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20513] {algorithm,
## dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20514] {algorithm,
## dataset,
## improv,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20515] {algorithm,
## improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20516] {network,
## improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20517] {network,
## algorithm,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20518] {network,
## algorithm,
## neural,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [20519] {network,
## algorithm,
## improv,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20520] {improv,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20521] {dataset,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20522] {dataset,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20523] {dataset,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20524] {dataset,
## improv,
## neural,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [20525] {improv,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20526] {network,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20527] {network,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20528] {network,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20529] {network,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20530] {dataset,
## improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20531] {network,
## improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20532] {network,
## dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20533] {network,
## dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20534] {network,
## dataset,
## improv,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20535] {algorithm,
## improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20536] {algorithm,
## dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20537] {dataset,
## improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20538] {algorithm,
## dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20539] {algorithm,
## dataset,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20540] {algorithm,
## improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20541] {network,
## algorithm,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20542] {network,
## improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20543] {network,
## algorithm,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20544] {network,
## algorithm,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20545] {algorithm,
## dataset,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20546] {network,
## algorithm,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20547] {network,
## dataset,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20548] {network,
## algorithm,
## dataset,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20549] {network,
## algorithm,
## dataset,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [20550] {dataset,
## improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20551] {network,
## improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20552] {network,
## dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20553] {network,
## dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20554] {network,
## dataset,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20555] {approach,
## algorithm,
## neural,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [20556] {show,
## algorithm,
## neural,
## process} => {approach} 0.1000000 1.0000000 2.500000 3
## [20557] {approach,
## show,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20558] {approach,
## show,
## algorithm,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20559] {approach,
## show,
## algorithm,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20560] {approach,
## algorithm,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20561] {network,
## algorithm,
## neural,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [20562] {approach,
## network,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20563] {approach,
## network,
## algorithm,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20564] {approach,
## network,
## algorithm,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20565] {algorithm,
## neural,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [20566] {network,
## algorithm,
## neural,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [20567] {network,
## neural,
## process,
## work} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20568] {network,
## algorithm,
## process,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [20569] {network,
## algorithm,
## neural,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [20570] {algorithm,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20571] {algorithm,
## dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20572] {dataset,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20573] {algorithm,
## dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20574] {algorithm,
## dataset,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20575] {algorithm,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20576] {network,
## algorithm,
## neural,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [20577] {network,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20578] {network,
## algorithm,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20579] {network,
## algorithm,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20580] {algorithm,
## dataset,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20581] {network,
## algorithm,
## neural,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [20582] {network,
## dataset,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20583] {network,
## algorithm,
## dataset,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20584] {network,
## algorithm,
## dataset,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20585] {show,
## algorithm,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20586] {network,
## algorithm,
## neural,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [20587] {network,
## show,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20588] {network,
## show,
## algorithm,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20589] {network,
## show,
## algorithm,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20590] {featur,
## algorithm,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20591] {network,
## algorithm,
## neural,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [20592] {featur,
## network,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20593] {featur,
## network,
## algorithm,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20594] {featur,
## network,
## algorithm,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20595] {approach,
## show,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20596] {approach,
## network,
## neural,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [20597] {network,
## show,
## neural,
## process} => {approach} 0.1000000 1.0000000 2.500000 3
## [20598] {approach,
## network,
## show,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20599] {approach,
## network,
## show,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20600] {dataset,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20601] {network,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20602] {network,
## dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20603] {network,
## dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20604] {network,
## dataset,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20605] {approach,
## show,
## algorithm,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20606] {approach,
## network,
## algorithm,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [20607] {network,
## show,
## algorithm,
## process} => {approach} 0.1000000 1.0000000 2.500000 3
## [20608] {approach,
## network,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20609] {approach,
## network,
## show,
## algorithm} => {process} 0.1000000 1.0000000 5.000000 3
## [20610] {algorithm,
## dataset,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20611] {network,
## algorithm,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20612] {network,
## algorithm,
## dataset,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20613] {network,
## dataset,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20614] {network,
## algorithm,
## dataset,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20615] {model,
## show,
## algorithm,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [20616] {featur,
## show,
## algorithm,
## process} => {model} 0.1000000 1.0000000 1.875000 3
## [20617] {featur,
## model,
## algorithm,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [20618] {featur,
## model,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20619] {featur,
## model,
## show,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [20620] {approach,
## perform,
## demonstr,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20621] {approach,
## dataset,
## demonstr,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [20622] {dataset,
## perform,
## demonstr,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [20623] {approach,
## dataset,
## perform,
## demonstr} => {problem} 0.1000000 1.0000000 3.333333 3
## [20624] {approach,
## dataset,
## perform,
## problem} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [20625] {model,
## dataset,
## demonstr,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20626] {featur,
## dataset,
## demonstr,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [20627] {featur,
## model,
## dataset,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [20628] {featur,
## model,
## demonstr,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20629] {data,
## reduc,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [20630] {network,
## reduc,
## task,
## achiev} => {data} 0.1000000 1.0000000 2.307692 3
## [20631] {data,
## network,
## reduc,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [20632] {data,
## network,
## reduc,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20633] {data,
## network,
## task,
## achiev} => {reduc} 0.1000000 1.0000000 4.285714 3
## [20634] {data,
## paper,
## reduc,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20635] {network,
## paper,
## reduc,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20636] {data,
## network,
## paper,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [20637] {data,
## network,
## reduc,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [20638] {data,
## network,
## paper,
## task} => {reduc} 0.1000000 1.0000000 4.285714 3
## [20639] {approach,
## method,
## reduc,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [20640] {approach,
## method,
## network,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [20641] {method,
## network,
## reduc,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [20642] {approach,
## network,
## reduc,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [20643] {approach,
## method,
## network,
## show} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20644] {data,
## reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20645] {data,
## reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [20646] {reduc,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20647] {data,
## reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20648] {data,
## reduc,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20649] {data,
## featur,
## reduc,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [20650] {featur,
## reduc,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20651] {data,
## featur,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [20652] {data,
## reduc,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20653] {data,
## network,
## reduc,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [20654] {network,
## reduc,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20655] {data,
## network,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [20656] {data,
## network,
## represent,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20657] {data,
## reduc,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20658] {data,
## featur,
## reduc,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20659] {featur,
## reduc,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20660] {data,
## featur,
## reduc,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20661] {data,
## reduc,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20662] {data,
## network,
## reduc,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [20663] {network,
## reduc,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20664] {data,
## network,
## reduc,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20665] {data,
## network,
## show,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20666] {data,
## featur,
## reduc,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20667] {data,
## network,
## reduc,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [20668] {featur,
## network,
## reduc,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20669] {data,
## featur,
## network,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [20670] {data,
## featur,
## network,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20671] {reduc,
## represent,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20672] {featur,
## reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20673] {featur,
## reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [20674] {featur,
## reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20675] {featur,
## represent,
## show,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20676] {reduc,
## represent,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20677] {network,
## reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20678] {network,
## reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [20679] {network,
## reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20680] {network,
## represent,
## show,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20681] {featur,
## reduc,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20682] {network,
## reduc,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20683] {featur,
## network,
## reduc,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [20684] {featur,
## network,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [20685] {featur,
## network,
## represent,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20686] {featur,
## reduc,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20687] {network,
## reduc,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20688] {featur,
## network,
## reduc,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20689] {featur,
## network,
## reduc,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20690] {featur,
## network,
## show,
## task} => {reduc} 0.1000000 1.0000000 4.285714 3
## [20691] {data,
## reduc,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [20692] {data,
## featur,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [20693] {data,
## featur,
## reduc,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [20694] {featur,
## reduc,
## represent,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [20695] {data,
## featur,
## represent,
## show} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20696] {data,
## reduc,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [20697] {data,
## network,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [20698] {data,
## network,
## reduc,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [20699] {network,
## reduc,
## represent,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [20700] {data,
## network,
## represent,
## show} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20701] {data,
## featur,
## reduc,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [20702] {data,
## network,
## reduc,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [20703] {data,
## featur,
## network,
## reduc} => {represent} 0.1000000 1.0000000 2.000000 3
## [20704] {featur,
## network,
## reduc,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [20705] {data,
## featur,
## network,
## represent} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20706] {data,
## featur,
## reduc,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [20707] {data,
## network,
## reduc,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [20708] {data,
## featur,
## network,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [20709] {featur,
## network,
## reduc,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [20710] {data,
## featur,
## network,
## show} => {reduc} 0.1000000 1.0000000 4.285714 3
## [20711] {featur,
## reduc,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [20712] {network,
## reduc,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [20713] {featur,
## network,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [20714] {featur,
## network,
## reduc,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [20715] {featur,
## network,
## represent,
## show} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20716] {paper,
## train,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20717] {paper,
## represent,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20718] {paper,
## represent,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [20719] {represent,
## train,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [20720] {paper,
## represent,
## train,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20721] {paper,
## train,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20722] {featur,
## paper,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20723] {featur,
## paper,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [20724] {featur,
## train,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [20725] {featur,
## paper,
## train,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20726] {paper,
## represent,
## train,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [20727] {featur,
## paper,
## train,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [20728] {featur,
## paper,
## represent,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [20729] {featur,
## represent,
## train,
## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [20730] {featur,
## paper,
## represent,
## train} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20731] {paper,
## represent,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20732] {featur,
## paper,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20733] {featur,
## paper,
## represent,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20734] {featur,
## represent,
## achiev,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [20735] {featur,
## paper,
## represent,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20736] {achiev,
## dataset,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [20737] {achiev,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20738] {achiev,
## dataset,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [20739] {achiev,
## dataset,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [20740] {dataset,
## improv,
## neural,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20741] {achiev,
## dataset,
## improv,
## neural} => {model} 0.1000000 1.0000000 1.875000 3
## [20742] {model,
## achiev,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20743] {model,
## achiev,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [20744] {model,
## achiev,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [20745] {model,
## dataset,
## improv,
## neural} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20746] {achiev,
## improv,
## neural,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [20747] {model,
## achiev,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [20748] {model,
## achiev,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [20749] {model,
## achiev,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [20750] {model,
## improv,
## neural,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20751] {achiev,
## dataset,
## improv,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [20752] {model,
## achiev,
## dataset,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [20753] {model,
## achiev,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20754] {model,
## achiev,
## dataset,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [20755] {model,
## dataset,
## improv,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20756] {approach,
## achiev,
## neural,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [20757] {achiev,
## neural,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [20758] {approach,
## achiev,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [20759] {approach,
## achiev,
## neural,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [20760] {approach,
## neural,
## propos,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20761] {approach,
## achiev,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [20762] {network,
## achiev,
## neural,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [20763] {approach,
## network,
## achiev,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [20764] {approach,
## network,
## achiev,
## neural} => {result} 0.1000000 1.0000000 3.000000 3
## [20765] {approach,
## network,
## neural,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20766] {achiev,
## neural,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [20767] {network,
## achiev,
## neural,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [20768] {network,
## achiev,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [20769] {network,
## achiev,
## neural,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [20770] {network,
## neural,
## propos,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20771] {approach,
## achiev,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [20772] {approach,
## network,
## achiev,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [20773] {network,
## achiev,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [20774] {approach,
## network,
## achiev,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [20775] {approach,
## network,
## propos,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20776] {approach,
## achiev,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20777] {approach,
## network,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [20778] {network,
## achiev,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [20779] {approach,
## network,
## achiev,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [20780] {achiev,
## dataset,
## neural,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [20781] {model,
## achiev,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [20782] {model,
## achiev,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20783] {model,
## achiev,
## dataset,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [20784] {model,
## dataset,
## neural,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20785] {approach,
## method,
## achiev,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [20786] {approach,
## featur,
## method,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [20787] {featur,
## method,
## achiev,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [20788] {approach,
## featur,
## achiev,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [20789] {approach,
## featur,
## method,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20790] {approach,
## method,
## achiev,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20791] {approach,
## method,
## network,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [20792] {method,
## network,
## achiev,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [20793] {approach,
## network,
## achiev,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [20794] {approach,
## method,
## network,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20795] {approach,
## featur,
## method,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [20796] {approach,
## method,
## network,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [20797] {featur,
## method,
## network,
## achiev} => {approach} 0.1000000 1.0000000 2.500000 3
## [20798] {approach,
## featur,
## network,
## achiev} => {method} 0.1000000 1.0000000 2.727273 3
## [20799] {approach,
## featur,
## method,
## network} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20800] {featur,
## method,
## achiev,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20801] {method,
## network,
## achiev,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [20802] {featur,
## method,
## network,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [20803] {featur,
## network,
## achiev,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [20804] {featur,
## method,
## network,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20805] {represent,
## task,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20806] {featur,
## task,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20807] {featur,
## represent,
## task,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20808] {featur,
## represent,
## achiev,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [20809] {represent,
## task,
## achiev,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [20810] {network,
## task,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20811] {network,
## represent,
## task,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20812] {network,
## represent,
## achiev,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [20813] {network,
## represent,
## task,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20814] {featur,
## task,
## achiev,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [20815] {network,
## task,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20816] {featur,
## network,
## task,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20817] {featur,
## network,
## achiev,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [20818] {featur,
## network,
## task,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20819] {featur,
## represent,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [20820] {network,
## represent,
## task,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [20821] {featur,
## network,
## task,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [20822] {featur,
## network,
## represent,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [20823] {featur,
## network,
## represent,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20824] {represent,
## train,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20825] {featur,
## train,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20826] {featur,
## represent,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20827] {featur,
## represent,
## achiev,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [20828] {featur,
## represent,
## train,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20829] {approach,
## achiev,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [20830] {approach,
## model,
## achiev,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [20831] {approach,
## model,
## achiev,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20832] {model,
## achiev,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [20833] {approach,
## achiev,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20834] {approach,
## network,
## achiev,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [20835] {approach,
## network,
## achiev,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [20836] {network,
## achiev,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [20837] {approach,
## model,
## achiev,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [20838] {approach,
## network,
## achiev,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [20839] {approach,
## model,
## network,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20840] {model,
## network,
## achiev,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [20841] {approach,
## model,
## network,
## dataset} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20842] {approach,
## model,
## achiev,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20843] {approach,
## network,
## achiev,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [20844] {approach,
## model,
## network,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [20845] {model,
## network,
## achiev,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [20846] {approach,
## model,
## network,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20847] {approach,
## featur,
## achiev,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20848] {approach,
## network,
## achiev,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [20849] {approach,
## featur,
## network,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [20850] {featur,
## network,
## achiev,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [20851] {approach,
## featur,
## network,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20852] {data,
## achiev,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20853] {data,
## represent,
## achiev,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [20854] {data,
## represent,
## achiev,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20855] {represent,
## achiev,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [20856] {data,
## achiev,
## dataset,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20857] {data,
## featur,
## achiev,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [20858] {data,
## featur,
## achiev,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20859] {featur,
## achiev,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [20860] {data,
## represent,
## achiev,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [20861] {data,
## featur,
## achiev,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [20862] {data,
## featur,
## represent,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20863] {featur,
## represent,
## achiev,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [20864] {data,
## represent,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20865] {data,
## featur,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20866] {data,
## featur,
## represent,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20867] {featur,
## represent,
## achiev,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [20868] {represent,
## achiev,
## dataset,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20869] {featur,
## achiev,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20870] {featur,
## represent,
## achiev,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [20871] {featur,
## represent,
## achiev,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [20872] {featur,
## represent,
## dataset,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20873] {featur,
## show,
## achiev,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [20874] {network,
## show,
## achiev,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [20875] {featur,
## network,
## achiev,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [20876] {featur,
## network,
## show,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20877] {featur,
## network,
## show,
## dataset} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20878] {model,
## achiev,
## dataset,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [20879] {featur,
## achiev,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [20880] {featur,
## model,
## achiev,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [20881] {featur,
## model,
## achiev,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20882] {model,
## achiev,
## dataset,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [20883] {network,
## achiev,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [20884] {model,
## network,
## achiev,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [20885] {model,
## network,
## achiev,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20886] {model,
## network,
## dataset,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20887] {represent,
## achiev,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20888] {featur,
## represent,
## achiev,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [20889] {featur,
## achiev,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20890] {featur,
## represent,
## achiev,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [20891] {featur,
## represent,
## achiev,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [20892] {network,
## represent,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20893] {featur,
## network,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20894] {featur,
## network,
## represent,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20895] {featur,
## network,
## represent,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20896] {data,
## task,
## object,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [20897] {task,
## object,
## propos,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [20898] {data,
## object,
## propos,
## optim} => {task} 0.1000000 1.0000000 2.727273 3
## [20899] {data,
## task,
## propos,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [20900] {data,
## task,
## object,
## propos} => {optim} 0.1000000 0.7500000 3.214286 3
## [20901] {data,
## task,
## object,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [20902] {featur,
## task,
## object,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [20903] {data,
## featur,
## object,
## optim} => {task} 0.1000000 1.0000000 2.727273 3
## [20904] {data,
## featur,
## task,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [20905] {data,
## featur,
## task,
## object} => {optim} 0.1000000 0.7500000 3.214286 3
## [20906] {task,
## object,
## propos,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [20907] {featur,
## task,
## object,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [20908] {featur,
## object,
## propos,
## optim} => {task} 0.1000000 1.0000000 2.727273 3
## [20909] {featur,
## task,
## propos,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [20910] {featur,
## task,
## object,
## propos} => {optim} 0.1000000 0.7500000 3.214286 3
## [20911] {data,
## object,
## propos,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [20912] {data,
## featur,
## object,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [20913] {featur,
## object,
## propos,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [20914] {data,
## featur,
## propos,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [20915] {data,
## featur,
## object,
## propos} => {optim} 0.1000000 0.7500000 3.214286 3
## [20916] {architectur,
## improv,
## optim,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [20917] {architectur,
## improv,
## perform,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [20918] {architectur,
## perform,
## optim,
## work} => {improv} 0.1000000 1.0000000 3.333333 3
## [20919] {improv,
## perform,
## optim,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20920] {architectur,
## improv,
## perform,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [20921] {architectur,
## improv,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [20922] {network,
## architectur,
## improv,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [20923] {network,
## architectur,
## optim,
## work} => {improv} 0.1000000 1.0000000 3.333333 3
## [20924] {network,
## improv,
## optim,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20925] {network,
## architectur,
## improv,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [20926] {architectur,
## improv,
## perform,
## optim} => {network} 0.1000000 1.0000000 1.578947 3
## [20927] {network,
## architectur,
## improv,
## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [20928] {network,
## architectur,
## perform,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [20929] {network,
## improv,
## perform,
## optim} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20930] {network,
## architectur,
## improv,
## perform} => {optim} 0.1000000 0.7500000 3.214286 3
## [20931] {architectur,
## perform,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [20932] {network,
## architectur,
## optim,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [20933] {network,
## architectur,
## perform,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [20934] {network,
## perform,
## optim,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20935] {network,
## architectur,
## perform,
## work} => {optim} 0.1000000 0.7500000 3.214286 3
## [20936] {train,
## algorithm,
## optim,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [20937] {show,
## algorithm,
## optim,
## problem} => {train} 0.1000000 1.0000000 2.500000 3
## [20938] {show,
## train,
## optim,
## problem} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20939] {show,
## train,
## algorithm,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [20940] {show,
## train,
## algorithm,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [20941] {algorithm,
## perform,
## optim,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [20942] {algorithm,
## propos,
## optim,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [20943] {perform,
## propos,
## optim,
## problem} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20944] {algorithm,
## perform,
## propos,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [20945] {algorithm,
## perform,
## propos,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [20946] {improv,
## perform,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [20947] {network,
## improv,
## optim,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [20948] {network,
## improv,
## perform,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [20949] {network,
## perform,
## optim,
## work} => {improv} 0.1000000 1.0000000 3.333333 3
## [20950] {network,
## improv,
## perform,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [20951] {data,
## algorithm,
## perform,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [20952] {featur,
## algorithm,
## perform,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [20953] {data,
## featur,
## algorithm,
## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [20954] {data,
## featur,
## perform,
## optim} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20955] {data,
## featur,
## algorithm,
## perform} => {optim} 0.1000000 1.0000000 4.285714 3
## [20956] {data,
## task,
## propos,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [20957] {data,
## featur,
## task,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [20958] {featur,
## task,
## propos,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [20959] {data,
## featur,
## propos,
## optim} => {task} 0.1000000 1.0000000 2.727273 3
## [20960] {paper,
## task,
## train,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [20961] {paper,
## task,
## learn,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [20962] {paper,
## train,
## learn,
## signific} => {task} 0.1000000 1.0000000 2.727273 3
## [20963] {task,
## train,
## learn,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [20964] {paper,
## task,
## train,
## learn} => {signific} 0.1000000 1.0000000 3.750000 3
## [20965] {paper,
## task,
## train,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [20966] {featur,
## paper,
## task,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [20967] {featur,
## paper,
## train,
## signific} => {task} 0.1000000 1.0000000 2.727273 3
## [20968] {featur,
## task,
## train,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [20969] {featur,
## paper,
## task,
## train} => {signific} 0.1000000 0.7500000 2.812500 3
## [20970] {paper,
## task,
## learn,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [20971] {featur,
## paper,
## task,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [20972] {featur,
## paper,
## learn,
## signific} => {task} 0.1000000 1.0000000 2.727273 3
## [20973] {featur,
## task,
## learn,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [20974] {featur,
## paper,
## task,
## learn} => {signific} 0.1000000 1.0000000 3.750000 3
## [20975] {paper,
## train,
## learn,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [20976] {featur,
## paper,
## train,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [20977] {featur,
## paper,
## learn,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [20978] {featur,
## train,
## learn,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [20979] {featur,
## paper,
## train,
## learn} => {signific} 0.1000000 0.7500000 2.812500 3
## [20980] {represent,
## train,
## result,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [20981] {featur,
## train,
## result,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [20982] {featur,
## represent,
## result,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [20983] {featur,
## represent,
## train,
## signific} => {result} 0.1000000 1.0000000 3.000000 3
## [20984] {featur,
## represent,
## train,
## result} => {signific} 0.1000000 1.0000000 3.750000 3
## [20985] {represent,
## train,
## result,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [20986] {network,
## train,
## result,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [20987] {network,
## represent,
## result,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [20988] {network,
## represent,
## train,
## signific} => {result} 0.1000000 1.0000000 3.000000 3
## [20989] {network,
## represent,
## train,
## result} => {signific} 0.1000000 0.7500000 2.812500 3
## [20990] {featur,
## train,
## result,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [20991] {network,
## train,
## result,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [20992] {featur,
## network,
## result,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [20993] {featur,
## network,
## train,
## signific} => {result} 0.1000000 1.0000000 3.000000 3
## [20994] {featur,
## network,
## train,
## result} => {signific} 0.1000000 1.0000000 3.750000 3
## [20995] {featur,
## represent,
## result,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [20996] {network,
## represent,
## result,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [20997] {featur,
## network,
## result,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [20998] {featur,
## network,
## represent,
## signific} => {result} 0.1000000 1.0000000 3.000000 3
## [20999] {featur,
## network,
## represent,
## result} => {signific} 0.1000000 1.0000000 3.750000 3
## [21000] {task,
## train,
## learn,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [21001] {featur,
## task,
## train,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [21002] {featur,
## task,
## learn,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [21003] {featur,
## train,
## learn,
## signific} => {task} 0.1000000 1.0000000 2.727273 3
## [21004] {featur,
## task,
## train,
## learn} => {signific} 0.1000000 1.0000000 3.750000 3
## [21005] {featur,
## represent,
## train,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [21006] {network,
## represent,
## train,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [21007] {featur,
## network,
## train,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [21008] {featur,
## network,
## represent,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [21009] {featur,
## network,
## represent,
## train} => {signific} 0.1000000 0.7500000 2.812500 3
## [21010] {represent,
## object,
## propos,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [21011] {featur,
## represent,
## object,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21012] {featur,
## object,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21013] {featur,
## represent,
## propos,
## success} => {object} 0.1000000 0.7500000 2.812500 3
## [21014] {featur,
## represent,
## object,
## propos} => {success} 0.1000000 1.0000000 3.750000 3
## [21015] {approach,
## make,
## represent,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21016] {approach,
## make,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21017] {make,
## represent,
## propos,
## success} => {approach} 0.1000000 1.0000000 2.500000 3
## [21018] {approach,
## represent,
## propos,
## success} => {make} 0.1000000 0.7500000 2.500000 3
## [21019] {approach,
## make,
## represent,
## propos} => {success} 0.1000000 0.7500000 2.812500 3
## [21020] {represent,
## perform,
## problem,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21021] {perform,
## propos,
## problem,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21022] {represent,
## propos,
## problem,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [21023] {represent,
## perform,
## propos,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [21024] {represent,
## perform,
## propos,
## problem} => {success} 0.1000000 0.7500000 2.812500 3
## [21025] {approach,
## method,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21026] {approach,
## method,
## represent,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [21027] {method,
## represent,
## learn,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [21028] {approach,
## represent,
## learn,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [21029] {approach,
## method,
## represent,
## learn} => {success} 0.1000000 1.0000000 3.750000 3
## [21030] {approach,
## method,
## learn,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21031] {approach,
## method,
## propos,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [21032] {method,
## propos,
## learn,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [21033] {approach,
## propos,
## learn,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [21034] {approach,
## method,
## propos,
## learn} => {success} 0.1000000 1.0000000 3.750000 3
## [21035] {approach,
## method,
## represent,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21036] {approach,
## method,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21037] {method,
## represent,
## propos,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [21038] {approach,
## represent,
## propos,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [21039] {approach,
## method,
## represent,
## propos} => {success} 0.1000000 1.0000000 3.750000 3
## [21040] {method,
## represent,
## learn,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [21041] {method,
## show,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21042] {method,
## represent,
## show,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [21043] {represent,
## show,
## learn,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [21044] {method,
## represent,
## show,
## learn} => {success} 0.1000000 1.0000000 3.750000 3
## [21045] {method,
## represent,
## learn,
## success} => {propos} 0.1333333 1.0000000 2.000000 4
## [21046] {method,
## propos,
## learn,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [21047] {method,
## represent,
## propos,
## success} => {learn} 0.1333333 1.0000000 2.307692 4
## [21048] {represent,
## propos,
## learn,
## success} => {method} 0.1333333 1.0000000 2.727273 4
## [21049] {method,
## represent,
## propos,
## learn} => {success} 0.1333333 1.0000000 3.750000 4
## [21050] {method,
## represent,
## learn,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [21051] {featur,
## method,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21052] {featur,
## method,
## represent,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [21053] {featur,
## represent,
## learn,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [21054] {featur,
## method,
## represent,
## learn} => {success} 0.1000000 1.0000000 3.750000 3
## [21055] {method,
## show,
## learn,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21056] {method,
## propos,
## learn,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [21057] {method,
## show,
## propos,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [21058] {show,
## propos,
## learn,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [21059] {method,
## show,
## propos,
## learn} => {success} 0.1000000 1.0000000 3.750000 3
## [21060] {method,
## propos,
## learn,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [21061] {featur,
## method,
## learn,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21062] {featur,
## method,
## propos,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [21063] {featur,
## propos,
## learn,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [21064] {featur,
## method,
## propos,
## learn} => {success} 0.1000000 1.0000000 3.750000 3
## [21065] {method,
## represent,
## show,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21066] {method,
## represent,
## propos,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [21067] {method,
## show,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21068] {represent,
## show,
## propos,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [21069] {method,
## represent,
## show,
## propos} => {success} 0.1000000 1.0000000 3.750000 3
## [21070] {method,
## represent,
## propos,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [21071] {featur,
## method,
## represent,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21072] {featur,
## method,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21073] {featur,
## represent,
## propos,
## success} => {method} 0.1000000 0.7500000 2.045455 3
## [21074] {featur,
## method,
## represent,
## propos} => {success} 0.1000000 1.0000000 3.750000 3
## [21075] {approach,
## represent,
## task,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21076] {approach,
## task,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21077] {represent,
## task,
## propos,
## success} => {approach} 0.1000000 1.0000000 2.500000 3
## [21078] {approach,
## represent,
## propos,
## success} => {task} 0.1000000 0.7500000 2.045455 3
## [21079] {approach,
## represent,
## task,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [21080] {approach,
## featur,
## task,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21081] {featur,
## represent,
## task,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [21082] {approach,
## featur,
## represent,
## success} => {task} 0.1000000 1.0000000 2.727273 3
## [21083] {approach,
## task,
## propos,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [21084] {approach,
## featur,
## task,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21085] {featur,
## task,
## propos,
## success} => {approach} 0.1000000 1.0000000 2.500000 3
## [21086] {approach,
## featur,
## propos,
## success} => {task} 0.1000000 1.0000000 2.727273 3
## [21087] {approach,
## featur,
## task,
## propos} => {success} 0.1000000 0.7500000 2.812500 3
## [21088] {data,
## represent,
## task,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [21089] {data,
## featur,
## task,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21090] {featur,
## represent,
## task,
## success} => {data} 0.1000000 0.7500000 1.730769 3
## [21091] {data,
## featur,
## represent,
## success} => {task} 0.1000000 1.0000000 2.727273 3
## [21092] {represent,
## task,
## learn,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [21093] {featur,
## task,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21094] {featur,
## represent,
## task,
## success} => {learn} 0.1000000 0.7500000 1.730769 3
## [21095] {featur,
## represent,
## learn,
## success} => {task} 0.1000000 0.7500000 2.045455 3
## [21096] {represent,
## task,
## propos,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [21097] {featur,
## represent,
## task,
## success} => {propos} 0.1000000 0.7500000 1.500000 3
## [21098] {featur,
## task,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21099] {featur,
## represent,
## propos,
## success} => {task} 0.1000000 0.7500000 2.045455 3
## [21100] {featur,
## represent,
## task,
## propos} => {success} 0.1000000 0.7500000 2.812500 3
## [21101] {approach,
## represent,
## learn,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21102] {approach,
## propos,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21103] {approach,
## represent,
## propos,
## success} => {learn} 0.1000000 0.7500000 1.730769 3
## [21104] {represent,
## propos,
## learn,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [21105] {approach,
## represent,
## propos,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [21106] {approach,
## featur,
## represent,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [21107] {approach,
## featur,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21108] {featur,
## represent,
## propos,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [21109] {approach,
## featur,
## represent,
## propos} => {success} 0.1000000 0.7500000 2.812500 3
## [21110] {represent,
## show,
## learn,
## success} => {propos} 0.1000000 0.7500000 1.500000 3
## [21111] {represent,
## propos,
## learn,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [21112] {show,
## propos,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21113] {represent,
## show,
## propos,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [21114] {represent,
## show,
## propos,
## learn} => {success} 0.1000000 0.7500000 2.812500 3
## [21115] {represent,
## show,
## learn,
## success} => {model} 0.1000000 0.7500000 1.406250 3
## [21116] {model,
## represent,
## learn,
## success} => {show} 0.1000000 1.0000000 1.875000 3
## [21117] {model,
## show,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21118] {model,
## represent,
## show,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [21119] {model,
## represent,
## show,
## learn} => {success} 0.1000000 0.7500000 2.812500 3
## [21120] {represent,
## show,
## learn,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [21121] {featur,
## represent,
## learn,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [21122] {featur,
## show,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21123] {featur,
## represent,
## show,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [21124] {represent,
## propos,
## learn,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [21125] {featur,
## represent,
## learn,
## success} => {propos} 0.1000000 0.7500000 1.500000 3
## [21126] {featur,
## propos,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [21127] {featur,
## represent,
## propos,
## success} => {learn} 0.1000000 0.7500000 1.730769 3
## [21128] {classif,
## architectur,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21129] {classif,
## method,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21130] {method,
## architectur,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21131] {classif,
## method,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21132] {classif,
## method,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21133] {classif,
## architectur,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21134] {approach,
## classif,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21135] {approach,
## architectur,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21136] {approach,
## classif,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21137] {approach,
## classif,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21138] {classif,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21139] {classif,
## architectur,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [21140] {architectur,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21141] {classif,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21142] {classif,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21143] {classif,
## architectur,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21144] {classif,
## network,
## architectur,
## experi} => {neural} 0.1000000 0.7500000 2.250000 3
## [21145] {network,
## architectur,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21146] {classif,
## network,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21147] {classif,
## network,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21148] {classif,
## method,
## architectur,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [21149] {approach,
## classif,
## architectur,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [21150] {approach,
## method,
## architectur,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [21151] {approach,
## classif,
## method,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21152] {approach,
## classif,
## method,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [21153] {classif,
## method,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21154] {classif,
## architectur,
## experi,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [21155] {method,
## architectur,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21156] {classif,
## method,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21157] {classif,
## method,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21158] {classif,
## method,
## architectur,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [21159] {classif,
## network,
## architectur,
## experi} => {method} 0.1000000 0.7500000 2.045455 3
## [21160] {method,
## network,
## architectur,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [21161] {classif,
## method,
## network,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21162] {classif,
## method,
## network,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [21163] {approach,
## classif,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21164] {classif,
## architectur,
## experi,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [21165] {approach,
## architectur,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21166] {approach,
## classif,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21167] {approach,
## classif,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21168] {approach,
## classif,
## architectur,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [21169] {classif,
## network,
## architectur,
## experi} => {approach} 0.1000000 0.7500000 1.875000 3
## [21170] {approach,
## network,
## architectur,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [21171] {approach,
## classif,
## network,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21172] {approach,
## classif,
## network,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [21173] {classif,
## architectur,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21174] {classif,
## architectur,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21175] {architectur,
## dataset,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21176] {classif,
## dataset,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21177] {classif,
## architectur,
## dataset,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21178] {classif,
## architectur,
## dataset,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [21179] {classif,
## network,
## architectur,
## experi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21180] {network,
## architectur,
## dataset,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [21181] {classif,
## network,
## dataset,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21182] {classif,
## network,
## architectur,
## dataset} => {experi} 0.1000000 1.0000000 3.750000 3
## [21183] {classif,
## architectur,
## experi,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [21184] {classif,
## featur,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21185] {featur,
## architectur,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21186] {classif,
## featur,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21187] {classif,
## featur,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21188] {classif,
## architectur,
## experi,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [21189] {classif,
## network,
## architectur,
## experi} => {propos} 0.1333333 1.0000000 2.000000 4
## [21190] {network,
## architectur,
## experi,
## propos} => {classif} 0.1333333 1.0000000 3.750000 4
## [21191] {classif,
## network,
## experi,
## propos} => {architectur} 0.1333333 1.0000000 3.750000 4
## [21192] {classif,
## network,
## architectur,
## propos} => {experi} 0.1333333 1.0000000 3.750000 4
## [21193] {classif,
## featur,
## architectur,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [21194] {classif,
## network,
## architectur,
## experi} => {featur} 0.1000000 0.7500000 1.406250 3
## [21195] {featur,
## network,
## architectur,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [21196] {classif,
## featur,
## network,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21197] {classif,
## featur,
## network,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [21198] {method,
## architectur,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21199] {approach,
## architectur,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21200] {approach,
## method,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21201] {approach,
## method,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21202] {approach,
## method,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21203] {method,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21204] {architectur,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21205] {method,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21206] {method,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21207] {method,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21208] {method,
## architectur,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21209] {network,
## architectur,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21210] {method,
## network,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21211] {method,
## network,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21212] {method,
## network,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21213] {approach,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21214] {architectur,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21215] {approach,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21216] {approach,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21217] {approach,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21218] {approach,
## architectur,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21219] {network,
## architectur,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21220] {approach,
## network,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21221] {approach,
## network,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21222] {approach,
## network,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21223] {architectur,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21224] {network,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21225] {network,
## architectur,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [21226] {network,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21227] {network,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21228] {approach,
## method,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21229] {method,
## architectur,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21230] {approach,
## architectur,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21231] {approach,
## method,
## experi,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21232] {approach,
## method,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21233] {approach,
## method,
## architectur,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [21234] {method,
## network,
## architectur,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [21235] {approach,
## network,
## architectur,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [21236] {approach,
## method,
## network,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21237] {approach,
## method,
## network,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [21238] {method,
## architectur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21239] {method,
## network,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21240] {network,
## architectur,
## experi,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [21241] {method,
## network,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21242] {method,
## network,
## architectur,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21243] {approach,
## architectur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21244] {approach,
## network,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21245] {network,
## architectur,
## experi,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [21246] {approach,
## network,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21247] {approach,
## network,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21248] {architectur,
## dataset,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21249] {network,
## architectur,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21250] {network,
## architectur,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21251] {network,
## dataset,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21252] {network,
## architectur,
## dataset,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21253] {featur,
## architectur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21254] {network,
## architectur,
## experi,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [21255] {featur,
## network,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21256] {featur,
## network,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21257] {featur,
## network,
## architectur,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21258] {classif,
## method,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21259] {approach,
## classif,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21260] {approach,
## classif,
## method,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21261] {approach,
## method,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21262] {approach,
## classif,
## method,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21263] {classif,
## method,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21264] {classif,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21265] {classif,
## method,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21266] {method,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21267] {classif,
## method,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21268] {classif,
## method,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21269] {classif,
## network,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21270] {classif,
## method,
## network,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21271] {method,
## network,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21272] {classif,
## method,
## network,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21273] {approach,
## classif,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21274] {classif,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21275] {approach,
## classif,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21276] {approach,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21277] {approach,
## classif,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21278] {approach,
## classif,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21279] {classif,
## network,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21280] {approach,
## classif,
## network,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21281] {approach,
## network,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21282] {approach,
## classif,
## network,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21283] {classif,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21284] {classif,
## network,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21285] {classif,
## network,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [21286] {network,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21287] {classif,
## network,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21288] {approach,
## classif,
## method,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21289] {classif,
## method,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21290] {approach,
## classif,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21291] {approach,
## method,
## experi,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [21292] {approach,
## classif,
## method,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21293] {approach,
## classif,
## method,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [21294] {classif,
## method,
## network,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [21295] {approach,
## classif,
## network,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [21296] {approach,
## method,
## network,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [21297] {approach,
## classif,
## method,
## network} => {experi} 0.1000000 0.7500000 2.812500 3
## [21298] {classif,
## method,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21299] {classif,
## method,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21300] {classif,
## network,
## experi,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [21301] {method,
## network,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21302] {classif,
## method,
## network,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21303] {approach,
## classif,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21304] {approach,
## classif,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21305] {classif,
## network,
## experi,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [21306] {approach,
## network,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21307] {approach,
## classif,
## network,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21308] {classif,
## dataset,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21309] {classif,
## network,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21310] {classif,
## network,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21311] {network,
## dataset,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21312] {classif,
## network,
## dataset,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21313] {classif,
## featur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21314] {classif,
## network,
## experi,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [21315] {classif,
## featur,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21316] {featur,
## network,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21317] {classif,
## featur,
## network,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21318] {approach,
## method,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21319] {method,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21320] {approach,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21321] {approach,
## method,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [21322] {approach,
## method,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21323] {approach,
## method,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21324] {method,
## network,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21325] {approach,
## network,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21326] {approach,
## method,
## network,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21327] {approach,
## method,
## network,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21328] {method,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21329] {method,
## network,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21330] {network,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21331] {method,
## network,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21332] {method,
## network,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21333] {approach,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21334] {approach,
## network,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21335] {network,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21336] {approach,
## network,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21337] {method,
## algorithm,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21338] {method,
## show,
## algorithm,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21339] {method,
## show,
## experi,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [21340] {show,
## algorithm,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [21341] {method,
## show,
## algorithm,
## perform} => {experi} 0.1000000 1.0000000 3.750000 3
## [21342] {approach,
## method,
## experi,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21343] {approach,
## method,
## dataset,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21344] {method,
## dataset,
## experi,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [21345] {approach,
## dataset,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [21346] {approach,
## method,
## dataset,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [21347] {approach,
## method,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21348] {approach,
## method,
## show,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21349] {method,
## show,
## experi,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [21350] {approach,
## show,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [21351] {approach,
## method,
## show,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [21352] {approach,
## method,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21353] {approach,
## method,
## experi,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [21354] {method,
## experi,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21355] {approach,
## experi,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21356] {approach,
## method,
## perform,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21357] {approach,
## method,
## dataset,
## experi} => {show} 0.1000000 1.0000000 1.875000 3
## [21358] {approach,
## method,
## show,
## experi} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21359] {method,
## show,
## dataset,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [21360] {approach,
## show,
## dataset,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [21361] {approach,
## method,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21362] {approach,
## method,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21363] {method,
## dataset,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21364] {approach,
## dataset,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21365] {approach,
## method,
## dataset,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21366] {approach,
## method,
## show,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21367] {approach,
## method,
## experi,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [21368] {method,
## show,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21369] {approach,
## show,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21370] {approach,
## method,
## show,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21371] {approach,
## method,
## experi,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [21372] {approach,
## method,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21373] {method,
## network,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21374] {approach,
## network,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21375] {approach,
## method,
## network,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21376] {method,
## dataset,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21377] {method,
## show,
## experi,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21378] {method,
## show,
## dataset,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21379] {show,
## dataset,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [21380] {method,
## show,
## dataset,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [21381] {method,
## dataset,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21382] {method,
## experi,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21383] {method,
## dataset,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21384] {dataset,
## experi,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21385] {method,
## dataset,
## perform,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21386] {method,
## show,
## experi,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [21387] {method,
## experi,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [21388] {method,
## show,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21389] {show,
## experi,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21390] {method,
## show,
## perform,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21391] {method,
## show,
## experi,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [21392] {method,
## model,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21393] {method,
## model,
## show,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21394] {model,
## show,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [21395] {method,
## model,
## show,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [21396] {method,
## show,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21397] {method,
## dataset,
## experi,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [21398] {method,
## show,
## experi,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21399] {show,
## dataset,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21400] {method,
## show,
## dataset,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21401] {approach,
## dataset,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21402] {approach,
## show,
## experi,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21403] {approach,
## show,
## dataset,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21404] {show,
## dataset,
## experi,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [21405] {approach,
## show,
## dataset,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [21406] {approach,
## dataset,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21407] {approach,
## experi,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21408] {approach,
## dataset,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21409] {dataset,
## experi,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21410] {approach,
## dataset,
## perform,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21411] {approach,
## show,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21412] {approach,
## experi,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [21413] {approach,
## show,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21414] {show,
## experi,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21415] {approach,
## show,
## perform,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21416] {approach,
## show,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21417] {approach,
## dataset,
## experi,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [21418] {approach,
## show,
## experi,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21419] {show,
## dataset,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21420] {show,
## dataset,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21421] {dataset,
## experi,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [21422] {show,
## experi,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21423] {show,
## dataset,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21424] {show,
## dataset,
## perform,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21425] {classif,
## object,
## propos,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [21426] {classif,
## featur,
## object,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [21427] {classif,
## featur,
## object,
## propos} => {recognit} 0.1000000 0.7500000 2.500000 3
## [21428] {featur,
## object,
## propos,
## recognit} => {classif} 0.1000000 1.0000000 3.750000 3
## [21429] {classif,
## featur,
## propos,
## recognit} => {object} 0.1000000 1.0000000 3.750000 3
## [21430] {classif,
## show,
## object,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21431] {classif,
## object,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [21432] {classif,
## show,
## object,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21433] {show,
## object,
## perform,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [21434] {classif,
## show,
## perform,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [21435] {classif,
## show,
## object,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [21436] {classif,
## featur,
## object,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21437] {classif,
## featur,
## show,
## object} => {perform} 0.1000000 1.0000000 2.142857 3
## [21438] {featur,
## show,
## object,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [21439] {classif,
## featur,
## show,
## perform} => {object} 0.1000000 0.7500000 2.812500 3
## [21440] {classif,
## object,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21441] {classif,
## featur,
## object,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21442] {classif,
## featur,
## object,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [21443] {featur,
## object,
## perform,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [21444] {classif,
## featur,
## perform,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [21445] {classif,
## object,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [21446] {classif,
## featur,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [21447] {classif,
## featur,
## object,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [21448] {featur,
## object,
## propos,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [21449] {classif,
## featur,
## propos,
## learn} => {object} 0.1000000 0.7500000 2.812500 3
## [21450] {classif,
## show,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21451] {classif,
## featur,
## show,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [21452] {classif,
## featur,
## object,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [21453] {featur,
## show,
## object,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [21454] {classif,
## featur,
## show,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [21455] {classif,
## model,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21456] {classif,
## featur,
## object,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [21457] {classif,
## featur,
## model,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [21458] {featur,
## model,
## object,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [21459] {classif,
## featur,
## model,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [21460] {object,
## perform,
## propos,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [21461] {featur,
## object,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [21462] {featur,
## object,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [21463] {featur,
## object,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [21464] {featur,
## perform,
## propos,
## problem} => {object} 0.1000000 0.7500000 2.812500 3
## [21465] {show,
## object,
## perform,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [21466] {object,
## perform,
## propos,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [21467] {show,
## object,
## propos,
## recognit} => {perform} 0.1000000 1.0000000 2.142857 3
## [21468] {show,
## object,
## perform,
## propos} => {recognit} 0.1000000 0.7500000 2.500000 3
## [21469] {show,
## perform,
## propos,
## recognit} => {object} 0.1000000 1.0000000 3.750000 3
## [21470] {method,
## show,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21471] {featur,
## method,
## show,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [21472] {featur,
## method,
## object,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [21473] {featur,
## show,
## object,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [21474] {featur,
## method,
## show,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [21475] {algorithm,
## object,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21476] {featur,
## algorithm,
## object,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21477] {featur,
## algorithm,
## object,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21478] {featur,
## object,
## perform,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [21479] {featur,
## algorithm,
## perform,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [21480] {data,
## task,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [21481] {data,
## task,
## object,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [21482] {task,
## object,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [21483] {data,
## object,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [21484] {data,
## task,
## object,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [21485] {data,
## model,
## task,
## object} => {learn} 0.1000000 1.0000000 2.307692 3
## [21486] {model,
## task,
## object,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [21487] {data,
## model,
## object,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [21488] {data,
## task,
## object,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [21489] {data,
## featur,
## task,
## object} => {learn} 0.1000000 0.7500000 1.730769 3
## [21490] {featur,
## task,
## object,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [21491] {data,
## featur,
## object,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [21492] {data,
## task,
## object,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [21493] {data,
## model,
## task,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [21494] {model,
## task,
## object,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [21495] {data,
## model,
## object,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [21496] {data,
## model,
## task,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [21497] {data,
## task,
## object,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [21498] {data,
## featur,
## task,
## object} => {propos} 0.1333333 1.0000000 2.000000 4
## [21499] {featur,
## task,
## object,
## propos} => {data} 0.1333333 1.0000000 2.307692 4
## [21500] {data,
## featur,
## object,
## propos} => {task} 0.1333333 1.0000000 2.727273 4
## [21501] {data,
## featur,
## task,
## propos} => {object} 0.1333333 0.8000000 3.000000 4
## [21502] {data,
## model,
## task,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [21503] {data,
## featur,
## task,
## object} => {model} 0.1000000 0.7500000 1.406250 3
## [21504] {featur,
## model,
## task,
## object} => {data} 0.1000000 1.0000000 2.307692 3
## [21505] {data,
## featur,
## model,
## object} => {task} 0.1000000 1.0000000 2.727273 3
## [21506] {task,
## object,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [21507] {model,
## task,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [21508] {model,
## task,
## object,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [21509] {model,
## object,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [21510] {model,
## task,
## propos,
## learn} => {object} 0.1000000 0.7500000 2.812500 3
## [21511] {task,
## object,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [21512] {featur,
## task,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [21513] {featur,
## task,
## object,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [21514] {featur,
## object,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [21515] {model,
## task,
## object,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [21516] {featur,
## task,
## object,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [21517] {featur,
## model,
## task,
## object} => {learn} 0.1000000 1.0000000 2.307692 3
## [21518] {featur,
## model,
## object,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [21519] {model,
## task,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21520] {featur,
## task,
## object,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [21521] {featur,
## model,
## task,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [21522] {featur,
## model,
## object,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [21523] {featur,
## model,
## task,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [21524] {show,
## object,
## perform,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [21525] {featur,
## show,
## object,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21526] {featur,
## object,
## perform,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [21527] {featur,
## show,
## object,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [21528] {featur,
## show,
## perform,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [21529] {data,
## object,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [21530] {data,
## model,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [21531] {data,
## model,
## object,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [21532] {model,
## object,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [21533] {data,
## object,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [21534] {data,
## featur,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [21535] {data,
## featur,
## object,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [21536] {featur,
## object,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [21537] {data,
## model,
## object,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [21538] {data,
## featur,
## object,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [21539] {data,
## featur,
## model,
## object} => {learn} 0.1000000 1.0000000 2.307692 3
## [21540] {featur,
## model,
## object,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [21541] {data,
## model,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21542] {data,
## featur,
## object,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [21543] {data,
## featur,
## model,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [21544] {featur,
## model,
## object,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [21545] {model,
## dataset,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21546] {featur,
## dataset,
## object,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [21547] {featur,
## model,
## dataset,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [21548] {featur,
## model,
## object,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21549] {model,
## dataset,
## object,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21550] {network,
## dataset,
## object,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [21551] {model,
## network,
## dataset,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [21552] {model,
## network,
## object,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21553] {model,
## network,
## dataset,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [21554] {featur,
## dataset,
## object,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21555] {network,
## dataset,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21556] {featur,
## network,
## dataset,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [21557] {featur,
## network,
## object,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21558] {featur,
## network,
## dataset,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [21559] {featur,
## model,
## dataset,
## object} => {network} 0.1000000 1.0000000 1.578947 3
## [21560] {model,
## network,
## dataset,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [21561] {featur,
## network,
## dataset,
## object} => {model} 0.1000000 1.0000000 1.875000 3
## [21562] {featur,
## model,
## network,
## object} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21563] {featur,
## model,
## network,
## dataset} => {object} 0.1000000 1.0000000 3.750000 3
## [21564] {show,
## object,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [21565] {featur,
## show,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [21566] {featur,
## object,
## propos,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [21567] {featur,
## show,
## object,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [21568] {featur,
## show,
## propos,
## learn} => {object} 0.1000000 1.0000000 3.750000 3
## [21569] {model,
## object,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [21570] {featur,
## object,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [21571] {featur,
## model,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [21572] {featur,
## model,
## object,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [21573] {model,
## show,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21574] {featur,
## show,
## object,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [21575] {featur,
## model,
## show,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [21576] {featur,
## model,
## object,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [21577] {featur,
## model,
## show,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [21578] {featur,
## model,
## object,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [21579] {model,
## network,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21580] {featur,
## network,
## object,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [21581] {featur,
## model,
## network,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [21582] {featur,
## model,
## network,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [21583] {classif,
## method,
## architectur,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21584] {approach,
## classif,
## architectur,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21585] {approach,
## classif,
## method,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [21586] {approach,
## method,
## architectur,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21587] {approach,
## classif,
## method,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21588] {classif,
## method,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21589] {classif,
## architectur,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21590] {classif,
## method,
## architectur,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21591] {method,
## architectur,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21592] {classif,
## method,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21593] {classif,
## method,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21594] {classif,
## network,
## architectur,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21595] {classif,
## method,
## network,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [21596] {method,
## network,
## architectur,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21597] {classif,
## method,
## network,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21598] {approach,
## classif,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21599] {classif,
## architectur,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21600] {approach,
## classif,
## architectur,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21601] {approach,
## architectur,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21602] {approach,
## classif,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21603] {approach,
## classif,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21604] {classif,
## network,
## architectur,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21605] {approach,
## classif,
## network,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [21606] {approach,
## network,
## architectur,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21607] {approach,
## classif,
## network,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21608] {classif,
## architectur,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21609] {classif,
## network,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21610] {classif,
## network,
## architectur,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [21611] {network,
## architectur,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21612] {classif,
## network,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21613] {approach,
## classif,
## method,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [21614] {classif,
## method,
## architectur,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21615] {approach,
## classif,
## architectur,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21616] {approach,
## method,
## architectur,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21617] {approach,
## classif,
## method,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21618] {approach,
## classif,
## method,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [21619] {classif,
## method,
## network,
## architectur} => {approach} 0.1000000 1.0000000 2.500000 3
## [21620] {approach,
## classif,
## network,
## architectur} => {method} 0.1000000 1.0000000 2.727273 3
## [21621] {approach,
## method,
## network,
## architectur} => {classif} 0.1000000 1.0000000 3.750000 3
## [21622] {approach,
## classif,
## method,
## network} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21623] {classif,
## method,
## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21624] {classif,
## method,
## network,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [21625] {classif,
## network,
## architectur,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [21626] {method,
## network,
## architectur,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [21627] {classif,
## method,
## network,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21628] {approach,
## classif,
## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21629] {approach,
## classif,
## network,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [21630] {classif,
## network,
## architectur,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [21631] {approach,
## network,
## architectur,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21632] {approach,
## classif,
## network,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21633] {classif,
## architectur,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21634] {classif,
## network,
## architectur,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [21635] {classif,
## network,
## architectur,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21636] {network,
## architectur,
## dataset,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [21637] {classif,
## network,
## dataset,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21638] {classif,
## featur,
## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21639] {classif,
## network,
## architectur,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [21640] {classif,
## featur,
## network,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [21641] {featur,
## network,
## architectur,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [21642] {classif,
## featur,
## network,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21643] {algorithm,
## architectur,
## improv,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [21644] {architectur,
## improv,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [21645] {algorithm,
## architectur,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [21646] {algorithm,
## architectur,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [21647] {algorithm,
## improv,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21648] {algorithm,
## architectur,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21649] {architectur,
## dataset,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [21650] {algorithm,
## architectur,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [21651] {algorithm,
## architectur,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [21652] {algorithm,
## dataset,
## improv,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21653] {algorithm,
## architectur,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21654] {network,
## architectur,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [21655] {network,
## algorithm,
## architectur,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [21656] {network,
## algorithm,
## architectur,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [21657] {network,
## algorithm,
## improv,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21658] {architectur,
## improv,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21659] {architectur,
## dataset,
## improv,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [21660] {architectur,
## dataset,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [21661] {architectur,
## dataset,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [21662] {dataset,
## improv,
## neural,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21663] {architectur,
## improv,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [21664] {network,
## architectur,
## improv,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [21665] {network,
## architectur,
## improv,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [21666] {network,
## architectur,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [21667] {network,
## improv,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21668] {architectur,
## dataset,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21669] {network,
## architectur,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21670] {network,
## architectur,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [21671] {network,
## architectur,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [21672] {network,
## dataset,
## improv,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21673] {algorithm,
## architectur,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21674] {algorithm,
## architectur,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [21675] {architectur,
## dataset,
## improv,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [21676] {algorithm,
## architectur,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [21677] {algorithm,
## dataset,
## improv,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21678] {algorithm,
## architectur,
## improv,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [21679] {network,
## algorithm,
## architectur,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [21680] {network,
## architectur,
## improv,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [21681] {network,
## algorithm,
## architectur,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [21682] {network,
## algorithm,
## improv,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21683] {algorithm,
## architectur,
## dataset,
## improv} => {network} 0.1000000 1.0000000 1.578947 3
## [21684] {network,
## algorithm,
## architectur,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21685] {network,
## architectur,
## dataset,
## improv} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [21686] {network,
## algorithm,
## architectur,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [21687] {network,
## algorithm,
## dataset,
## improv} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21688] {architectur,
## improv,
## perform,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [21689] {network,
## architectur,
## improv,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [21690] {network,
## architectur,
## improv,
## perform} => {work} 0.1000000 0.7500000 1.875000 3
## [21691] {network,
## architectur,
## perform,
## work} => {improv} 0.1000000 0.7500000 2.500000 3
## [21692] {network,
## improv,
## perform,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21693] {architectur,
## dataset,
## improv,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [21694] {network,
## architectur,
## improv,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21695] {network,
## architectur,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [21696] {network,
## architectur,
## dataset,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [21697] {network,
## dataset,
## improv,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21698] {featur,
## architectur,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [21699] {network,
## architectur,
## neural,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [21700] {featur,
## network,
## architectur,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [21701] {featur,
## network,
## architectur,
## neural} => {result} 0.1000000 1.0000000 3.000000 3
## [21702] {featur,
## network,
## neural,
## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21703] {approach,
## method,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21704] {method,
## architectur,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21705] {approach,
## architectur,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21706] {approach,
## method,
## architectur,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21707] {approach,
## method,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21708] {approach,
## method,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21709] {method,
## network,
## architectur,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21710] {approach,
## network,
## architectur,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21711] {approach,
## method,
## network,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [21712] {approach,
## method,
## network,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21713] {method,
## architectur,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21714] {method,
## network,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21715] {network,
## architectur,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21716] {method,
## network,
## architectur,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [21717] {method,
## network,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21718] {algorithm,
## architectur,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21719] {algorithm,
## architectur,
## dataset,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [21720] {architectur,
## dataset,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [21721] {algorithm,
## architectur,
## dataset,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [21722] {algorithm,
## dataset,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21723] {algorithm,
## architectur,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [21724] {network,
## algorithm,
## architectur,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [21725] {network,
## architectur,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [21726] {network,
## algorithm,
## architectur,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [21727] {network,
## algorithm,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21728] {algorithm,
## architectur,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21729] {network,
## algorithm,
## architectur,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21730] {network,
## architectur,
## dataset,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [21731] {network,
## algorithm,
## architectur,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [21732] {network,
## algorithm,
## dataset,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21733] {approach,
## architectur,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21734] {approach,
## network,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21735] {network,
## architectur,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21736] {approach,
## network,
## architectur,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21737] {architectur,
## dataset,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [21738] {network,
## architectur,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21739] {network,
## architectur,
## dataset,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [21740] {network,
## architectur,
## dataset,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [21741] {network,
## dataset,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21742] {approach,
## method,
## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21743] {approach,
## method,
## network,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [21744] {method,
## network,
## architectur,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [21745] {approach,
## network,
## architectur,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21746] {approach,
## method,
## network,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21747] {method,
## architectur,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21748] {method,
## architectur,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21749] {method,
## architectur,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21750] {architectur,
## dataset,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21751] {method,
## dataset,
## perform,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21752] {method,
## architectur,
## dataset,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [21753] {method,
## network,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21754] {method,
## network,
## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [21755] {network,
## architectur,
## dataset,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [21756] {method,
## network,
## dataset,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21757] {method,
## architectur,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21758] {method,
## network,
## architectur,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21759] {method,
## network,
## architectur,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [21760] {network,
## architectur,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21761] {method,
## network,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21762] {method,
## architectur,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21763] {method,
## network,
## architectur,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [21764] {method,
## network,
## architectur,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21765] {network,
## architectur,
## dataset,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [21766] {method,
## network,
## dataset,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21767] {featur,
## method,
## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21768] {method,
## network,
## architectur,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [21769] {featur,
## method,
## network,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [21770] {featur,
## network,
## architectur,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [21771] {featur,
## method,
## network,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21772] {algorithm,
## architectur,
## dataset,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [21773] {network,
## algorithm,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21774] {network,
## algorithm,
## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [21775] {network,
## architectur,
## dataset,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [21776] {network,
## algorithm,
## dataset,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21777] {architectur,
## dataset,
## perform,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [21778] {network,
## architectur,
## perform,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21779] {network,
## architectur,
## dataset,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [21780] {network,
## architectur,
## dataset,
## perform} => {work} 0.1000000 0.7500000 1.875000 3
## [21781] {network,
## dataset,
## perform,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21782] {architectur,
## dataset,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [21783] {network,
## architectur,
## dataset,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [21784] {network,
## architectur,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21785] {network,
## architectur,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [21786] {featur,
## architectur,
## dataset,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [21787] {network,
## architectur,
## dataset,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [21788] {featur,
## network,
## architectur,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21789] {featur,
## network,
## architectur,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [21790] {featur,
## network,
## dataset,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21791] {architectur,
## dataset,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21792] {network,
## architectur,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [21793] {network,
## architectur,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21794] {network,
## architectur,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [21795] {network,
## dataset,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21796] {featur,
## architectur,
## dataset,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [21797] {network,
## architectur,
## dataset,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [21798] {featur,
## network,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21799] {featur,
## network,
## architectur,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [21800] {featur,
## network,
## dataset,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21801] {featur,
## architectur,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21802] {network,
## architectur,
## dataset,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [21803] {featur,
## network,
## architectur,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [21804] {featur,
## network,
## architectur,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21805] {featur,
## network,
## dataset,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21806] {approach,
## classif,
## make,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [21807] {classif,
## featur,
## make,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [21808] {approach,
## classif,
## featur,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [21809] {approach,
## featur,
## make,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [21810] {approach,
## classif,
## featur,
## method} => {make} 0.1000000 0.7500000 2.500000 3
## [21811] {approach,
## make,
## perform,
## problem} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21812] {approach,
## make,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [21813] {make,
## dataset,
## perform,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [21814] {approach,
## make,
## dataset,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [21815] {approach,
## dataset,
## perform,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [21816] {approach,
## make,
## perform,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [21817] {approach,
## make,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [21818] {make,
## perform,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [21819] {approach,
## make,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [21820] {approach,
## perform,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [21821] {approach,
## make,
## perform,
## problem} => {represent} 0.1000000 0.7500000 1.500000 3
## [21822] {approach,
## make,
## represent,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [21823] {make,
## represent,
## perform,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [21824] {approach,
## make,
## represent,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [21825] {approach,
## represent,
## perform,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [21826] {approach,
## make,
## perform,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [21827] {approach,
## make,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [21828] {make,
## perform,
## propos,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [21829] {approach,
## make,
## perform,
## propos} => {problem} 0.1000000 1.0000000 3.333333 3
## [21830] {approach,
## perform,
## propos,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [21831] {approach,
## make,
## perform,
## problem} => {model} 0.1000000 0.7500000 1.406250 3
## [21832] {approach,
## make,
## model,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [21833] {make,
## model,
## perform,
## problem} => {approach} 0.1000000 0.7500000 1.875000 3
## [21834] {approach,
## make,
## model,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [21835] {approach,
## model,
## perform,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [21836] {approach,
## make,
## perform,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [21837] {approach,
## featur,
## make,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [21838] {featur,
## make,
## perform,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [21839] {approach,
## featur,
## make,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [21840] {approach,
## featur,
## perform,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [21841] {approach,
## make,
## dataset,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [21842] {approach,
## make,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21843] {make,
## dataset,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [21844] {approach,
## make,
## dataset,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [21845] {approach,
## dataset,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [21846] {approach,
## make,
## dataset,
## problem} => {model} 0.1000000 1.0000000 1.875000 3
## [21847] {approach,
## make,
## model,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21848] {make,
## model,
## dataset,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [21849] {approach,
## make,
## model,
## dataset} => {problem} 0.1000000 1.0000000 3.333333 3
## [21850] {approach,
## model,
## dataset,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [21851] {approach,
## make,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [21852] {approach,
## make,
## model,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [21853] {make,
## model,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [21854] {approach,
## make,
## model,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [21855] {approach,
## model,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [21856] {approach,
## make,
## represent,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [21857] {approach,
## make,
## propos,
## problem} => {represent} 0.1000000 1.0000000 2.000000 3
## [21858] {make,
## represent,
## propos,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [21859] {approach,
## make,
## represent,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [21860] {approach,
## represent,
## propos,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [21861] {make,
## dataset,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [21862] {make,
## perform,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21863] {make,
## dataset,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [21864] {make,
## dataset,
## perform,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [21865] {dataset,
## perform,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [21866] {make,
## dataset,
## perform,
## problem} => {model} 0.1000000 1.0000000 1.875000 3
## [21867] {make,
## model,
## perform,
## problem} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21868] {make,
## model,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [21869] {make,
## model,
## dataset,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [21870] {model,
## dataset,
## perform,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [21871] {make,
## perform,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [21872] {make,
## model,
## perform,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [21873] {make,
## model,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [21874] {make,
## model,
## perform,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [21875] {model,
## perform,
## problem,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [21876] {make,
## represent,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [21877] {make,
## perform,
## propos,
## problem} => {represent} 0.1000000 1.0000000 2.000000 3
## [21878] {make,
## represent,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [21879] {make,
## represent,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [21880] {represent,
## perform,
## propos,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [21881] {make,
## dataset,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [21882] {make,
## model,
## dataset,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [21883] {make,
## model,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21884] {make,
## model,
## dataset,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [21885] {model,
## dataset,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [21886] {approach,
## make,
## method,
## paper} => {represent} 0.1000000 1.0000000 2.000000 3
## [21887] {make,
## method,
## paper,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [21888] {approach,
## make,
## paper,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [21889] {approach,
## make,
## method,
## represent} => {paper} 0.1000000 1.0000000 3.000000 3
## [21890] {approach,
## method,
## paper,
## represent} => {make} 0.1000000 1.0000000 3.333333 3
## [21891] {make,
## method,
## paper,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [21892] {make,
## method,
## model,
## paper} => {show} 0.1000000 1.0000000 1.875000 3
## [21893] {make,
## model,
## paper,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [21894] {make,
## method,
## model,
## show} => {paper} 0.1000000 0.7500000 2.250000 3
## [21895] {method,
## model,
## paper,
## show} => {make} 0.1000000 1.0000000 3.333333 3
## [21896] {make,
## paper,
## represent,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [21897] {featur,
## make,
## paper,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [21898] {featur,
## make,
## paper,
## represent} => {train} 0.1000000 1.0000000 2.500000 3
## [21899] {featur,
## make,
## represent,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [21900] {featur,
## paper,
## represent,
## train} => {make} 0.1000000 0.7500000 2.500000 3
## [21901] {make,
## paper,
## represent,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [21902] {make,
## paper,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [21903] {make,
## paper,
## represent,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [21904] {make,
## represent,
## propos,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [21905] {paper,
## represent,
## propos,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [21906] {approach,
## make,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [21907] {approach,
## make,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [21908] {make,
## method,
## model,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [21909] {approach,
## make,
## model,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [21910] {make,
## method,
## show,
## perform} => {model} 0.1000000 1.0000000 1.875000 3
## [21911] {make,
## method,
## model,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21912] {make,
## method,
## model,
## show} => {perform} 0.1000000 0.7500000 1.607143 3
## [21913] {make,
## model,
## show,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [21914] {method,
## model,
## show,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [21915] {approach,
## data,
## make,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [21916] {approach,
## make,
## represent,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [21917] {data,
## make,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [21918] {approach,
## data,
## make,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [21919] {approach,
## data,
## make,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [21920] {approach,
## featur,
## make,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [21921] {data,
## featur,
## make,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [21922] {approach,
## data,
## featur,
## make} => {task} 0.1000000 1.0000000 2.727273 3
## [21923] {approach,
## data,
## featur,
## task} => {make} 0.1000000 0.7500000 2.500000 3
## [21924] {approach,
## make,
## represent,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [21925] {approach,
## make,
## task,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [21926] {make,
## represent,
## task,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21927] {approach,
## make,
## represent,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [21928] {approach,
## make,
## represent,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [21929] {approach,
## featur,
## make,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [21930] {featur,
## make,
## represent,
## task} => {approach} 0.1333333 1.0000000 2.500000 4
## [21931] {approach,
## featur,
## make,
## represent} => {task} 0.1333333 1.0000000 2.727273 4
## [21932] {approach,
## featur,
## represent,
## task} => {make} 0.1333333 0.8000000 2.666667 4
## [21933] {approach,
## make,
## task,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21934] {approach,
## featur,
## make,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [21935] {featur,
## make,
## task,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21936] {approach,
## featur,
## make,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [21937] {approach,
## featur,
## task,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [21938] {data,
## make,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [21939] {data,
## featur,
## make,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [21940] {featur,
## make,
## represent,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [21941] {data,
## featur,
## make,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [21942] {make,
## represent,
## task,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21943] {featur,
## make,
## represent,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [21944] {featur,
## make,
## task,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [21945] {featur,
## make,
## represent,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [21946] {featur,
## represent,
## task,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [21947] {approach,
## make,
## dataset,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [21948] {approach,
## make,
## perform,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21949] {approach,
## make,
## dataset,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [21950] {make,
## dataset,
## perform,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [21951] {approach,
## dataset,
## perform,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [21952] {approach,
## make,
## dataset,
## perform} => {model} 0.1000000 1.0000000 1.875000 3
## [21953] {approach,
## make,
## model,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21954] {approach,
## make,
## model,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [21955] {make,
## model,
## dataset,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [21956] {approach,
## model,
## dataset,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [21957] {approach,
## make,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [21958] {approach,
## make,
## model,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [21959] {approach,
## make,
## model,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [21960] {make,
## model,
## perform,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [21961] {approach,
## model,
## perform,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [21962] {approach,
## make,
## represent,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21963] {approach,
## make,
## perform,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [21964] {approach,
## make,
## represent,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [21965] {make,
## represent,
## perform,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [21966] {approach,
## represent,
## perform,
## propos} => {make} 0.1000000 1.0000000 3.333333 3
## [21967] {approach,
## data,
## make,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [21968] {approach,
## data,
## featur,
## make} => {represent} 0.1000000 1.0000000 2.000000 3
## [21969] {approach,
## featur,
## make,
## represent} => {data} 0.1000000 0.7500000 1.730769 3
## [21970] {data,
## featur,
## make,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [21971] {approach,
## data,
## featur,
## represent} => {make} 0.1000000 0.7500000 2.500000 3
## [21972] {approach,
## make,
## dataset,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [21973] {approach,
## make,
## model,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [21974] {approach,
## make,
## model,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21975] {make,
## model,
## dataset,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [21976] {approach,
## model,
## dataset,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [21977] {approach,
## make,
## represent,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [21978] {approach,
## make,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [21979] {approach,
## make,
## represent,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [21980] {make,
## represent,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [21981] {approach,
## make,
## represent,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [21982] {approach,
## featur,
## make,
## represent} => {propos} 0.1000000 0.7500000 1.500000 3
## [21983] {approach,
## featur,
## make,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [21984] {featur,
## make,
## represent,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [21985] {approach,
## featur,
## represent,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [21986] {data,
## make,
## represent,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21987] {data,
## make,
## perform,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [21988] {make,
## represent,
## perform,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [21989] {data,
## make,
## represent,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21990] {data,
## represent,
## perform,
## propos} => {make} 0.1000000 1.0000000 3.333333 3
## [21991] {data,
## make,
## represent,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [21992] {data,
## featur,
## make,
## perform} => {represent} 0.1000000 1.0000000 2.000000 3
## [21993] {featur,
## make,
## represent,
## perform} => {data} 0.1000000 1.0000000 2.307692 3
## [21994] {data,
## featur,
## make,
## represent} => {perform} 0.1000000 0.7500000 1.607143 3
## [21995] {data,
## featur,
## represent,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [21996] {data,
## make,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [21997] {data,
## featur,
## make,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21998] {featur,
## make,
## perform,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [21999] {data,
## featur,
## make,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [22000] {data,
## featur,
## perform,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [22001] {make,
## dataset,
## perform,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [22002] {make,
## represent,
## dataset,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [22003] {make,
## represent,
## perform,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22004] {make,
## represent,
## dataset,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22005] {represent,
## dataset,
## perform,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [22006] {make,
## dataset,
## perform,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [22007] {make,
## dataset,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [22008] {make,
## perform,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22009] {make,
## dataset,
## propos,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22010] {dataset,
## perform,
## propos,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [22011] {make,
## dataset,
## perform,
## learn} => {model} 0.1333333 1.0000000 1.875000 4
## [22012] {make,
## model,
## dataset,
## perform} => {learn} 0.1333333 1.0000000 2.307692 4
## [22013] {make,
## model,
## perform,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [22014] {make,
## model,
## dataset,
## learn} => {perform} 0.1333333 1.0000000 2.142857 4
## [22015] {model,
## dataset,
## perform,
## learn} => {make} 0.1333333 1.0000000 3.333333 4
## [22016] {make,
## dataset,
## perform,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22017] {featur,
## make,
## dataset,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [22018] {featur,
## make,
## perform,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22019] {featur,
## make,
## dataset,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22020] {featur,
## dataset,
## perform,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [22021] {make,
## represent,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [22022] {make,
## dataset,
## perform,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [22023] {make,
## represent,
## perform,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22024] {make,
## represent,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [22025] {represent,
## dataset,
## perform,
## propos} => {make} 0.1000000 1.0000000 3.333333 3
## [22026] {make,
## represent,
## dataset,
## perform} => {model} 0.1000000 1.0000000 1.875000 3
## [22027] {make,
## model,
## dataset,
## perform} => {represent} 0.1000000 0.7500000 1.500000 3
## [22028] {make,
## model,
## represent,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22029] {make,
## model,
## represent,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [22030] {model,
## represent,
## dataset,
## perform} => {make} 0.1000000 1.0000000 3.333333 3
## [22031] {make,
## dataset,
## perform,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [22032] {make,
## model,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [22033] {make,
## model,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22034] {make,
## model,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [22035] {model,
## dataset,
## perform,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [22036] {make,
## model,
## dataset,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [22037] {featur,
## make,
## dataset,
## perform} => {model} 0.1000000 1.0000000 1.875000 3
## [22038] {featur,
## make,
## model,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22039] {featur,
## make,
## model,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [22040] {featur,
## model,
## dataset,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [22041] {make,
## represent,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [22042] {make,
## perform,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22043] {make,
## represent,
## perform,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [22044] {make,
## represent,
## propos,
## learn} => {perform} 0.1000000 0.7500000 1.607143 3
## [22045] {represent,
## perform,
## propos,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [22046] {make,
## represent,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [22047] {make,
## model,
## perform,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [22048] {make,
## model,
## represent,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [22049] {make,
## model,
## represent,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22050] {model,
## represent,
## perform,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [22051] {make,
## perform,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [22052] {make,
## model,
## perform,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [22053] {make,
## model,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [22054] {make,
## model,
## propos,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22055] {model,
## perform,
## propos,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [22056] {make,
## model,
## perform,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22057] {featur,
## make,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [22058] {featur,
## make,
## model,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [22059] {featur,
## make,
## model,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22060] {featur,
## model,
## perform,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [22061] {make,
## represent,
## perform,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [22062] {make,
## model,
## represent,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [22063] {make,
## model,
## perform,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [22064] {make,
## model,
## represent,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [22065] {model,
## represent,
## perform,
## propos} => {make} 0.1000000 1.0000000 3.333333 3
## [22066] {make,
## represent,
## perform,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [22067] {featur,
## make,
## represent,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [22068] {featur,
## make,
## perform,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [22069] {featur,
## make,
## represent,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [22070] {featur,
## represent,
## perform,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [22071] {data,
## make,
## represent,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [22072] {data,
## featur,
## make,
## represent} => {propos} 0.1000000 0.7500000 1.500000 3
## [22073] {data,
## featur,
## make,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [22074] {featur,
## make,
## represent,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [22075] {data,
## featur,
## represent,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [22076] {data,
## make,
## model,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [22077] {data,
## featur,
## make,
## represent} => {model} 0.1000000 0.7500000 1.406250 3
## [22078] {data,
## featur,
## make,
## model} => {represent} 0.1000000 1.0000000 2.000000 3
## [22079] {featur,
## make,
## model,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [22080] {make,
## represent,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [22081] {make,
## dataset,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22082] {make,
## represent,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [22083] {make,
## represent,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22084] {make,
## represent,
## dataset,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [22085] {make,
## model,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [22086] {make,
## model,
## represent,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [22087] {make,
## model,
## represent,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22088] {model,
## represent,
## dataset,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [22089] {make,
## dataset,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [22090] {make,
## model,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [22091] {make,
## model,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [22092] {make,
## model,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22093] {make,
## model,
## dataset,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22094] {featur,
## make,
## dataset,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [22095] {featur,
## make,
## model,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [22096] {featur,
## make,
## model,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22097] {make,
## represent,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [22098] {make,
## model,
## represent,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [22099] {make,
## model,
## dataset,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [22100] {make,
## model,
## represent,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22101] {model,
## represent,
## dataset,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [22102] {make,
## represent,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [22103] {make,
## model,
## represent,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [22104] {make,
## model,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22105] {make,
## model,
## represent,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [22106] {model,
## represent,
## propos,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [22107] {make,
## represent,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22108] {featur,
## make,
## represent,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [22109] {featur,
## make,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22110] {featur,
## make,
## represent,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [22111] {classif,
## method,
## perform,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [22112] {classif,
## method,
## show,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22113] {classif,
## show,
## perform,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [22114] {classif,
## method,
## show,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [22115] {method,
## show,
## perform,
## problem} => {classif} 0.1000000 0.7500000 2.812500 3
## [22116] {classif,
## perform,
## problem,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [22117] {classif,
## show,
## perform,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [22118] {classif,
## show,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22119] {classif,
## show,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [22120] {show,
## perform,
## problem,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [22121] {classif,
## perform,
## problem,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [22122] {classif,
## featur,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [22123] {classif,
## featur,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22124] {classif,
## featur,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [22125] {featur,
## perform,
## problem,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [22126] {classif,
## show,
## perform,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [22127] {classif,
## perform,
## propos,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [22128] {classif,
## show,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22129] {classif,
## show,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [22130] {show,
## perform,
## propos,
## problem} => {classif} 0.1000000 0.7500000 2.812500 3
## [22131] {classif,
## show,
## perform,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [22132] {classif,
## featur,
## perform,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [22133] {classif,
## featur,
## show,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22134] {classif,
## featur,
## show,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [22135] {featur,
## show,
## perform,
## problem} => {classif} 0.1000000 1.0000000 3.750000 3
## [22136] {classif,
## show,
## problem,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [22137] {classif,
## featur,
## problem,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [22138] {classif,
## featur,
## show,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [22139] {classif,
## featur,
## show,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [22140] {featur,
## show,
## problem,
## learn} => {classif} 0.1000000 1.0000000 3.750000 3
## [22141] {classif,
## paper,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [22142] {classif,
## featur,
## paper,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [22143] {classif,
## featur,
## paper,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [22144] {classif,
## featur,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [22145] {featur,
## paper,
## task,
## train} => {classif} 0.1000000 0.7500000 2.812500 3
## [22146] {approach,
## classif,
## method,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [22147] {classif,
## method,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [22148] {approach,
## classif,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [22149] {approach,
## classif,
## method,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [22150] {approach,
## method,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [22151] {approach,
## classif,
## method,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22152] {classif,
## method,
## dataset,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [22153] {approach,
## classif,
## dataset,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [22154] {approach,
## classif,
## method,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [22155] {approach,
## method,
## dataset,
## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [22156] {approach,
## classif,
## method,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [22157] {classif,
## method,
## show,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [22158] {approach,
## classif,
## show,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [22159] {approach,
## classif,
## method,
## show} => {improv} 0.1000000 0.7500000 2.500000 3
## [22160] {approach,
## method,
## show,
## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [22161] {classif,
## method,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22162] {classif,
## method,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [22163] {classif,
## dataset,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [22164] {classif,
## method,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [22165] {method,
## dataset,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [22166] {classif,
## method,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [22167] {classif,
## method,
## show,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [22168] {classif,
## show,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [22169] {classif,
## method,
## show,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [22170] {method,
## show,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [22171] {classif,
## method,
## dataset,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [22172] {classif,
## method,
## show,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22173] {classif,
## show,
## dataset,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [22174] {classif,
## method,
## show,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [22175] {method,
## show,
## dataset,
## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [22176] {approach,
## classif,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22177] {approach,
## classif,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [22178] {classif,
## dataset,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [22179] {approach,
## classif,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [22180] {approach,
## dataset,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [22181] {approach,
## classif,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [22182] {approach,
## classif,
## show,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [22183] {classif,
## show,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [22184] {approach,
## classif,
## show,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [22185] {approach,
## show,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [22186] {approach,
## classif,
## dataset,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [22187] {approach,
## classif,
## show,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22188] {classif,
## show,
## dataset,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [22189] {approach,
## classif,
## show,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [22190] {approach,
## show,
## dataset,
## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [22191] {classif,
## dataset,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [22192] {classif,
## show,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22193] {classif,
## show,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [22194] {classif,
## show,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [22195] {show,
## dataset,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [22196] {approach,
## classif,
## method,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [22197] {classif,
## method,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [22198] {approach,
## classif,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [22199] {approach,
## classif,
## method,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22200] {approach,
## method,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [22201] {approach,
## classif,
## method,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [22202] {classif,
## method,
## network,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [22203] {approach,
## classif,
## network,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [22204] {approach,
## classif,
## method,
## network} => {neural} 0.1000000 0.7500000 2.250000 3
## [22205] {approach,
## method,
## network,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [22206] {classif,
## method,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [22207] {classif,
## method,
## network,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [22208] {classif,
## network,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [22209] {classif,
## method,
## network,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22210] {method,
## network,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [22211] {approach,
## classif,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [22212] {approach,
## classif,
## network,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [22213] {classif,
## network,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [22214] {approach,
## classif,
## network,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22215] {approach,
## classif,
## method,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [22216] {classif,
## method,
## network,
## train} => {approach} 0.1000000 1.0000000 2.500000 3
## [22217] {approach,
## classif,
## method,
## network} => {train} 0.1000000 0.7500000 1.875000 3
## [22218] {approach,
## classif,
## network,
## train} => {method} 0.1000000 1.0000000 2.727273 3
## [22219] {approach,
## method,
## network,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [22220] {approach,
## classif,
## method,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22221] {approach,
## classif,
## method,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [22222] {classif,
## method,
## dataset,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [22223] {approach,
## classif,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [22224] {approach,
## method,
## dataset,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [22225] {approach,
## classif,
## method,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [22226] {approach,
## classif,
## method,
## show} => {perform} 0.1000000 0.7500000 1.607143 3
## [22227] {classif,
## method,
## show,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [22228] {approach,
## classif,
## show,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [22229] {approach,
## method,
## show,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [22230] {approach,
## classif,
## method,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [22231] {approach,
## classif,
## method,
## show} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22232] {classif,
## method,
## show,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [22233] {approach,
## classif,
## show,
## dataset} => {method} 0.1000000 1.0000000 2.727273 3
## [22234] {approach,
## classif,
## method,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [22235] {approach,
## classif,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [22236] {classif,
## method,
## model,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [22237] {approach,
## classif,
## model,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [22238] {approach,
## classif,
## method,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [22239] {approach,
## classif,
## featur,
## method} => {show} 0.1000000 0.7500000 1.406250 3
## [22240] {classif,
## featur,
## method,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [22241] {approach,
## classif,
## featur,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [22242] {approach,
## featur,
## method,
## show} => {classif} 0.1000000 0.7500000 2.812500 3
## [22243] {approach,
## classif,
## method,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [22244] {approach,
## classif,
## method,
## network} => {show} 0.1000000 0.7500000 1.406250 3
## [22245] {classif,
## method,
## network,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [22246] {approach,
## classif,
## network,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [22247] {approach,
## method,
## network,
## show} => {classif} 0.1000000 0.7500000 2.812500 3
## [22248] {approach,
## classif,
## method,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [22249] {approach,
## classif,
## method,
## network} => {propos} 0.1000000 0.7500000 1.500000 3
## [22250] {classif,
## method,
## network,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [22251] {approach,
## classif,
## network,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [22252] {approach,
## method,
## network,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [22253] {approach,
## classif,
## method,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [22254] {approach,
## classif,
## featur,
## method} => {model} 0.1000000 0.7500000 1.406250 3
## [22255] {classif,
## featur,
## method,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [22256] {approach,
## classif,
## featur,
## model} => {method} 0.1000000 1.0000000 2.727273 3
## [22257] {approach,
## featur,
## method,
## model} => {classif} 0.1000000 0.7500000 2.812500 3
## [22258] {approach,
## classif,
## featur,
## method} => {network} 0.1000000 0.7500000 1.184211 3
## [22259] {approach,
## classif,
## method,
## network} => {featur} 0.1000000 0.7500000 1.406250 3
## [22260] {classif,
## featur,
## method,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [22261] {approach,
## classif,
## featur,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [22262] {approach,
## featur,
## method,
## network} => {classif} 0.1000000 0.7500000 2.812500 3
## [22263] {classif,
## method,
## dataset,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [22264] {classif,
## method,
## show,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22265] {classif,
## method,
## show,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [22266] {classif,
## show,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [22267] {method,
## show,
## dataset,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [22268] {classif,
## method,
## show,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [22269] {classif,
## method,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [22270] {classif,
## method,
## show,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [22271] {classif,
## show,
## perform,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [22272] {method,
## show,
## perform,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [22273] {classif,
## method,
## show,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [22274] {classif,
## featur,
## method,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [22275] {classif,
## featur,
## method,
## show} => {perform} 0.1000000 0.7500000 1.607143 3
## [22276] {classif,
## featur,
## show,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [22277] {featur,
## method,
## show,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [22278] {classif,
## method,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [22279] {classif,
## featur,
## method,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [22280] {classif,
## featur,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [22281] {classif,
## featur,
## model,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [22282] {featur,
## method,
## model,
## show} => {classif} 0.1000000 0.7500000 2.812500 3
## [22283] {classif,
## show,
## algorithm,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [22284] {classif,
## algorithm,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [22285] {classif,
## show,
## algorithm,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [22286] {classif,
## show,
## perform,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [22287] {show,
## algorithm,
## perform,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [22288] {classif,
## data,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [22289] {classif,
## data,
## featur,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [22290] {classif,
## featur,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [22291] {classif,
## data,
## featur,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [22292] {classif,
## task,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [22293] {classif,
## featur,
## task,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [22294] {classif,
## featur,
## task,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [22295] {classif,
## featur,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [22296] {approach,
## classif,
## dataset,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [22297] {approach,
## classif,
## show,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22298] {approach,
## classif,
## show,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [22299] {classif,
## show,
## dataset,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [22300] {approach,
## show,
## dataset,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [22301] {approach,
## classif,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [22302] {approach,
## classif,
## featur,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [22303] {approach,
## classif,
## featur,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [22304] {classif,
## featur,
## model,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [22305] {classif,
## show,
## perform,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [22306] {classif,
## featur,
## perform,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [22307] {classif,
## featur,
## show,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [22308] {classif,
## featur,
## show,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22309] {featur,
## show,
## perform,
## learn} => {classif} 0.1000000 1.0000000 3.750000 3
## [22310] {classif,
## show,
## perform,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [22311] {classif,
## featur,
## show,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [22312] {classif,
## featur,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [22313] {classif,
## featur,
## show,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [22314] {featur,
## show,
## perform,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [22315] {classif,
## model,
## show,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [22316] {classif,
## featur,
## show,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [22317] {classif,
## featur,
## model,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [22318] {classif,
## featur,
## model,
## show} => {perform} 0.1000000 0.7500000 1.607143 3
## [22319] {featur,
## model,
## show,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [22320] {approach,
## method,
## perform,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22321] {approach,
## method,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22322] {method,
## dataset,
## perform,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [22323] {approach,
## dataset,
## perform,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [22324] {approach,
## method,
## dataset,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [22325] {approach,
## method,
## perform,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [22326] {approach,
## method,
## show,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22327] {method,
## show,
## perform,
## problem} => {approach} 0.1000000 0.7500000 1.875000 3
## [22328] {approach,
## show,
## perform,
## problem} => {method} 0.1000000 1.0000000 2.727273 3
## [22329] {approach,
## method,
## show,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [22330] {approach,
## method,
## dataset,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [22331] {approach,
## method,
## show,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22332] {method,
## show,
## dataset,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [22333] {approach,
## show,
## dataset,
## problem} => {method} 0.1000000 1.0000000 2.727273 3
## [22334] {method,
## dataset,
## perform,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [22335] {method,
## show,
## perform,
## problem} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22336] {method,
## show,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22337] {show,
## dataset,
## perform,
## problem} => {method} 0.1000000 1.0000000 2.727273 3
## [22338] {method,
## show,
## dataset,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [22339] {method,
## perform,
## problem,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [22340] {method,
## show,
## perform,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [22341] {method,
## show,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22342] {show,
## perform,
## problem,
## learn} => {method} 0.1000000 0.7500000 2.045455 3
## [22343] {method,
## show,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [22344] {method,
## show,
## perform,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [22345] {method,
## perform,
## propos,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [22346] {method,
## show,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22347] {show,
## perform,
## propos,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [22348] {method,
## show,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [22349] {data,
## task,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [22350] {task,
## perform,
## propos,
## problem} => {data} 0.1000000 1.0000000 2.307692 3
## [22351] {data,
## task,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22352] {data,
## perform,
## propos,
## problem} => {task} 0.1000000 1.0000000 2.727273 3
## [22353] {data,
## task,
## perform,
## propos} => {problem} 0.1000000 1.0000000 3.333333 3
## [22354] {data,
## task,
## perform,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [22355] {featur,
## task,
## perform,
## problem} => {data} 0.1000000 1.0000000 2.307692 3
## [22356] {data,
## featur,
## task,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22357] {data,
## featur,
## perform,
## problem} => {task} 0.1000000 1.0000000 2.727273 3
## [22358] {data,
## featur,
## task,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [22359] {task,
## perform,
## propos,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [22360] {featur,
## task,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [22361] {featur,
## task,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22362] {featur,
## perform,
## propos,
## problem} => {task} 0.1000000 0.7500000 2.045455 3
## [22363] {featur,
## task,
## perform,
## propos} => {problem} 0.1000000 1.0000000 3.333333 3
## [22364] {data,
## task,
## propos,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [22365] {data,
## featur,
## task,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [22366] {featur,
## task,
## propos,
## problem} => {data} 0.1000000 1.0000000 2.307692 3
## [22367] {data,
## featur,
## propos,
## problem} => {task} 0.1000000 1.0000000 2.727273 3
## [22368] {approach,
## dataset,
## perform,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [22369] {approach,
## perform,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22370] {approach,
## dataset,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22371] {dataset,
## perform,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [22372] {approach,
## dataset,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [22373] {approach,
## dataset,
## perform,
## problem} => {show} 0.1000000 0.7500000 1.406250 3
## [22374] {approach,
## show,
## perform,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22375] {approach,
## show,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22376] {show,
## dataset,
## perform,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [22377] {approach,
## show,
## dataset,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [22378] {approach,
## dataset,
## perform,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [22379] {approach,
## perform,
## propos,
## problem} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22380] {approach,
## dataset,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22381] {dataset,
## perform,
## propos,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [22382] {approach,
## dataset,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [22383] {approach,
## dataset,
## perform,
## problem} => {model} 0.1000000 0.7500000 1.406250 3
## [22384] {approach,
## model,
## perform,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22385] {approach,
## model,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22386] {model,
## dataset,
## perform,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [22387] {approach,
## model,
## dataset,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [22388] {approach,
## perform,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [22389] {approach,
## model,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [22390] {approach,
## model,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22391] {model,
## perform,
## problem,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [22392] {approach,
## model,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [22393] {approach,
## represent,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [22394] {approach,
## perform,
## propos,
## problem} => {represent} 0.1000000 0.7500000 1.500000 3
## [22395] {approach,
## represent,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22396] {represent,
## perform,
## propos,
## problem} => {approach} 0.1000000 0.7500000 1.875000 3
## [22397] {approach,
## represent,
## perform,
## propos} => {problem} 0.1000000 1.0000000 3.333333 3
## [22398] {approach,
## dataset,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [22399] {approach,
## model,
## dataset,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [22400] {approach,
## model,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22401] {model,
## dataset,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [22402] {approach,
## model,
## dataset,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [22403] {data,
## perform,
## propos,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [22404] {data,
## featur,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [22405] {featur,
## perform,
## propos,
## problem} => {data} 0.1000000 0.7500000 1.730769 3
## [22406] {data,
## featur,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22407] {data,
## featur,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [22408] {dataset,
## perform,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [22409] {model,
## dataset,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [22410] {model,
## perform,
## problem,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22411] {model,
## dataset,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22412] {model,
## dataset,
## perform,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [22413] {represent,
## perform,
## problem,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [22414] {perform,
## propos,
## problem,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [22415] {represent,
## perform,
## propos,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [22416] {represent,
## propos,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22417] {represent,
## perform,
## propos,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [22418] {show,
## perform,
## problem,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [22419] {perform,
## propos,
## problem,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [22420] {show,
## perform,
## propos,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [22421] {show,
## propos,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22422] {show,
## perform,
## propos,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [22423] {show,
## perform,
## problem,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [22424] {model,
## perform,
## problem,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [22425] {model,
## show,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [22426] {model,
## show,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22427] {model,
## show,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [22428] {show,
## perform,
## problem,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22429] {featur,
## perform,
## problem,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [22430] {featur,
## show,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [22431] {featur,
## show,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22432] {featur,
## show,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [22433] {perform,
## propos,
## problem,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [22434] {model,
## perform,
## problem,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [22435] {model,
## perform,
## propos,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [22436] {model,
## propos,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22437] {model,
## perform,
## propos,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [22438] {perform,
## propos,
## problem,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22439] {featur,
## perform,
## problem,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [22440] {featur,
## perform,
## propos,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [22441] {featur,
## propos,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22442] {featur,
## perform,
## propos,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [22443] {model,
## perform,
## problem,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22444] {featur,
## perform,
## problem,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [22445] {featur,
## model,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [22446] {featur,
## model,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22447] {featur,
## model,
## perform,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [22448] {represent,
## perform,
## propos,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [22449] {featur,
## represent,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [22450] {featur,
## perform,
## propos,
## problem} => {represent} 0.1000000 0.7500000 1.500000 3
## [22451] {featur,
## represent,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [22452] {featur,
## represent,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [22453] {data,
## paper,
## task,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [22454] {paper,
## show,
## task,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [22455] {data,
## paper,
## show,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [22456] {data,
## paper,
## show,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [22457] {data,
## show,
## task,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [22458] {data,
## paper,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [22459] {featur,
## paper,
## task,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [22460] {data,
## featur,
## paper,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [22461] {data,
## featur,
## paper,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [22462] {data,
## featur,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [22463] {paper,
## task,
## train,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [22464] {featur,
## paper,
## task,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [22465] {featur,
## paper,
## task,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [22466] {featur,
## paper,
## train,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [22467] {featur,
## task,
## train,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [22468] {paper,
## represent,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [22469] {featur,
## paper,
## task,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [22470] {featur,
## paper,
## represent,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [22471] {featur,
## paper,
## represent,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [22472] {featur,
## represent,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [22473] {paper,
## represent,
## task,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [22474] {network,
## paper,
## task,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [22475] {network,
## paper,
## represent,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [22476] {network,
## paper,
## represent,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [22477] {network,
## represent,
## task,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [22478] {paper,
## show,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [22479] {featur,
## paper,
## task,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [22480] {featur,
## paper,
## show,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [22481] {featur,
## paper,
## show,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [22482] {featur,
## show,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [22483] {featur,
## paper,
## task,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [22484] {network,
## paper,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [22485] {featur,
## network,
## paper,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [22486] {featur,
## network,
## paper,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [22487] {featur,
## network,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [22488] {data,
## paper,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [22489] {data,
## featur,
## paper,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [22490] {featur,
## paper,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [22491] {data,
## featur,
## paper,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [22492] {featur,
## paper,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [22493] {network,
## paper,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [22494] {featur,
## network,
## paper,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [22495] {featur,
## network,
## paper,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [22496] {featur,
## network,
## represent,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [22497] {data,
## paper,
## train,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [22498] {data,
## featur,
## paper,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [22499] {featur,
## paper,
## train,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [22500] {data,
## featur,
## paper,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [22501] {data,
## featur,
## train,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [22502] {data,
## paper,
## represent,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [22503] {data,
## featur,
## paper,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [22504] {featur,
## paper,
## represent,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [22505] {data,
## featur,
## paper,
## represent} => {train} 0.1000000 1.0000000 2.500000 3
## [22506] {data,
## featur,
## represent,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [22507] {data,
## paper,
## show,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [22508] {data,
## featur,
## paper,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [22509] {featur,
## paper,
## show,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [22510] {data,
## featur,
## paper,
## show} => {train} 0.1000000 1.0000000 2.500000 3
## [22511] {data,
## featur,
## show,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [22512] {data,
## model,
## paper,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [22513] {data,
## featur,
## paper,
## train} => {model} 0.1000000 0.7500000 1.406250 3
## [22514] {featur,
## model,
## paper,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [22515] {data,
## featur,
## model,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [22516] {data,
## featur,
## model,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [22517] {paper,
## represent,
## train,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [22518] {featur,
## paper,
## train,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [22519] {featur,
## paper,
## represent,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [22520] {featur,
## paper,
## represent,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [22521] {featur,
## represent,
## train,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [22522] {paper,
## train,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [22523] {featur,
## paper,
## train,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [22524] {featur,
## paper,
## train,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [22525] {featur,
## paper,
## propos,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [22526] {featur,
## train,
## propos,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [22527] {featur,
## paper,
## represent,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [22528] {network,
## paper,
## represent,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [22529] {featur,
## network,
## paper,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [22530] {featur,
## network,
## paper,
## represent} => {train} 0.1000000 1.0000000 2.500000 3
## [22531] {featur,
## network,
## represent,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [22532] {paper,
## perform,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [22533] {model,
## paper,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [22534] {model,
## paper,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [22535] {model,
## paper,
## propos,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [22536] {model,
## perform,
## propos,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [22537] {data,
## task,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22538] {task,
## recognit,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22539] {data,
## recognit,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22540] {data,
## task,
## recognit,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [22541] {data,
## task,
## result,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [22542] {data,
## task,
## recognit,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [22543] {represent,
## task,
## recognit,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [22544] {data,
## represent,
## recognit,
## result} => {task} 0.1000000 1.0000000 2.727273 3
## [22545] {data,
## represent,
## task,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [22546] {data,
## represent,
## task,
## result} => {recognit} 0.1000000 0.7500000 2.500000 3
## [22547] {data,
## task,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [22548] {show,
## task,
## recognit,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [22549] {data,
## show,
## recognit,
## result} => {task} 0.1000000 1.0000000 2.727273 3
## [22550] {data,
## show,
## task,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [22551] {data,
## show,
## task,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [22552] {task,
## recognit,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22553] {represent,
## task,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22554] {represent,
## recognit,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22555] {represent,
## task,
## recognit,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [22556] {task,
## recognit,
## result,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [22557] {show,
## task,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22558] {show,
## recognit,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22559] {show,
## task,
## recognit,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [22560] {show,
## task,
## result,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [22561] {represent,
## task,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [22562] {show,
## task,
## recognit,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [22563] {represent,
## show,
## recognit,
## result} => {task} 0.1000000 1.0000000 2.727273 3
## [22564] {represent,
## show,
## task,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [22565] {represent,
## show,
## task,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [22566] {data,
## recognit,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22567] {data,
## represent,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22568] {represent,
## recognit,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22569] {data,
## represent,
## recognit,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [22570] {data,
## represent,
## result,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [22571] {data,
## recognit,
## result,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [22572] {data,
## show,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22573] {show,
## recognit,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22574] {data,
## show,
## recognit,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [22575] {data,
## show,
## result,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [22576] {data,
## represent,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [22577] {data,
## show,
## recognit,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [22578] {represent,
## show,
## recognit,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [22579] {data,
## represent,
## show,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [22580] {data,
## represent,
## show,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [22581] {show,
## dataset,
## recognit,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22582] {network,
## dataset,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [22583] {network,
## show,
## recognit,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22584] {network,
## show,
## dataset,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [22585] {network,
## show,
## dataset,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [22586] {represent,
## recognit,
## result,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [22587] {show,
## recognit,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22588] {represent,
## show,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22589] {represent,
## show,
## recognit,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [22590] {represent,
## show,
## result,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [22591] {data,
## task,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22592] {data,
## task,
## recognit,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22593] {task,
## dataset,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22594] {data,
## dataset,
## recognit,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [22595] {data,
## task,
## dataset,
## recognit} => {network} 0.1000000 1.0000000 1.578947 3
## [22596] {data,
## network,
## task,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22597] {network,
## task,
## dataset,
## recognit} => {data} 0.1000000 1.0000000 2.307692 3
## [22598] {data,
## network,
## dataset,
## recognit} => {task} 0.1000000 1.0000000 2.727273 3
## [22599] {data,
## network,
## task,
## dataset} => {recognit} 0.1000000 0.7500000 2.500000 3
## [22600] {data,
## task,
## recognit,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [22601] {data,
## represent,
## task,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22602] {represent,
## task,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22603] {data,
## represent,
## recognit,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [22604] {data,
## task,
## recognit,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [22605] {data,
## show,
## task,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22606] {show,
## task,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22607] {data,
## show,
## recognit,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22608] {data,
## task,
## recognit,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22609] {data,
## featur,
## task,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22610] {featur,
## task,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22611] {data,
## featur,
## recognit,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [22612] {data,
## task,
## recognit,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [22613] {data,
## network,
## task,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22614] {network,
## task,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22615] {data,
## network,
## recognit,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22616] {data,
## network,
## task,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [22617] {data,
## represent,
## task,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [22618] {data,
## show,
## task,
## recognit} => {represent} 0.1000000 1.0000000 2.000000 3
## [22619] {represent,
## show,
## task,
## recognit} => {data} 0.1000000 1.0000000 2.307692 3
## [22620] {data,
## represent,
## show,
## recognit} => {task} 0.1000000 1.0000000 2.727273 3
## [22621] {task,
## dataset,
## recognit,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [22622] {network,
## task,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22623] {network,
## task,
## recognit,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22624] {network,
## dataset,
## recognit,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22625] {network,
## task,
## dataset,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [22626] {represent,
## task,
## recognit,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [22627] {show,
## task,
## recognit,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22628] {represent,
## show,
## task,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22629] {represent,
## show,
## recognit,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [22630] {represent,
## show,
## task,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [22631] {data,
## train,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22632] {data,
## train,
## recognit,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22633] {train,
## dataset,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22634] {data,
## dataset,
## recognit,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [22635] {data,
## train,
## dataset,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [22636] {data,
## train,
## dataset,
## recognit} => {represent} 0.1000000 1.0000000 2.000000 3
## [22637] {data,
## represent,
## train,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22638] {represent,
## train,
## dataset,
## recognit} => {data} 0.1000000 1.0000000 2.307692 3
## [22639] {data,
## represent,
## dataset,
## recognit} => {train} 0.1000000 1.0000000 2.500000 3
## [22640] {data,
## represent,
## train,
## dataset} => {recognit} 0.1000000 0.7500000 2.500000 3
## [22641] {data,
## train,
## recognit,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22642] {data,
## represent,
## train,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22643] {represent,
## train,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22644] {data,
## represent,
## recognit,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [22645] {data,
## represent,
## train,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [22646] {train,
## dataset,
## recognit,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22647] {represent,
## train,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22648] {represent,
## train,
## recognit,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22649] {represent,
## dataset,
## recognit,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [22650] {represent,
## train,
## dataset,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [22651] {data,
## dataset,
## recognit,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [22652] {data,
## represent,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22653] {data,
## represent,
## recognit,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22654] {represent,
## dataset,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22655] {data,
## dataset,
## recognit,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [22656] {data,
## dataset,
## propos,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22657] {data,
## propos,
## recognit,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22658] {dataset,
## propos,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22659] {data,
## dataset,
## recognit,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22660] {data,
## featur,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22661] {data,
## featur,
## recognit,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22662] {featur,
## dataset,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22663] {data,
## dataset,
## recognit,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [22664] {data,
## network,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22665] {data,
## network,
## recognit,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22666] {network,
## dataset,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22667] {data,
## network,
## dataset,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [22668] {data,
## represent,
## recognit,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [22669] {data,
## show,
## recognit,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22670] {data,
## represent,
## show,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22671] {represent,
## show,
## recognit,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [22672] {data,
## represent,
## show,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [22673] {data,
## represent,
## recognit,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22674] {data,
## featur,
## recognit,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [22675] {data,
## featur,
## represent,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22676] {featur,
## represent,
## recognit,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [22677] {data,
## model,
## recognit,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [22678] {data,
## featur,
## recognit,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [22679] {data,
## featur,
## model,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22680] {featur,
## model,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22681] {model,
## dataset,
## propos,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [22682] {featur,
## dataset,
## propos,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [22683] {featur,
## model,
## dataset,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [22684] {featur,
## model,
## propos,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22685] {represent,
## show,
## recognit,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22686] {featur,
## represent,
## recognit,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [22687] {featur,
## show,
## recognit,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22688] {featur,
## represent,
## show,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [22689] {algorithm,
## improv,
## neural,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22690] {dataset,
## improv,
## neural,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22691] {algorithm,
## dataset,
## improv,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [22692] {algorithm,
## dataset,
## improv,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [22693] {algorithm,
## dataset,
## neural,
## result} => {improv} 0.1000000 1.0000000 3.333333 3
## [22694] {algorithm,
## improv,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22695] {network,
## improv,
## neural,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22696] {network,
## algorithm,
## improv,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [22697] {network,
## algorithm,
## improv,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [22698] {network,
## algorithm,
## neural,
## result} => {improv} 0.1000000 1.0000000 3.333333 3
## [22699] {dataset,
## improv,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22700] {network,
## improv,
## neural,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22701] {network,
## dataset,
## improv,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [22702] {network,
## dataset,
## improv,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [22703] {network,
## dataset,
## neural,
## result} => {improv} 0.1000000 0.7500000 2.500000 3
## [22704] {algorithm,
## dataset,
## improv,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22705] {network,
## algorithm,
## improv,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22706] {network,
## dataset,
## improv,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22707] {network,
## algorithm,
## dataset,
## improv} => {result} 0.1000000 0.7500000 2.250000 3
## [22708] {network,
## algorithm,
## dataset,
## result} => {improv} 0.1000000 1.0000000 3.333333 3
## [22709] {train,
## algorithm,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22710] {algorithm,
## dataset,
## improv,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [22711] {train,
## dataset,
## improv,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [22712] {train,
## algorithm,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [22713] {train,
## algorithm,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [22714] {train,
## algorithm,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [22715] {network,
## algorithm,
## improv,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [22716] {network,
## train,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22717] {network,
## train,
## algorithm,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [22718] {network,
## train,
## algorithm,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [22719] {approach,
## algorithm,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22720] {algorithm,
## dataset,
## improv,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [22721] {approach,
## dataset,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22722] {approach,
## algorithm,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [22723] {approach,
## algorithm,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [22724] {approach,
## algorithm,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [22725] {algorithm,
## improv,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [22726] {approach,
## improv,
## neural,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22727] {approach,
## algorithm,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22728] {approach,
## algorithm,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [22729] {approach,
## algorithm,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [22730] {network,
## algorithm,
## improv,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [22731] {approach,
## network,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22732] {approach,
## network,
## algorithm,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [22733] {approach,
## network,
## algorithm,
## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [22734] {algorithm,
## improv,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22735] {algorithm,
## dataset,
## improv,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [22736] {dataset,
## improv,
## neural,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [22737] {algorithm,
## dataset,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [22738] {algorithm,
## dataset,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [22739] {algorithm,
## improv,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [22740] {network,
## algorithm,
## improv,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [22741] {network,
## improv,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22742] {network,
## algorithm,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [22743] {network,
## algorithm,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [22744] {algorithm,
## dataset,
## improv,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [22745] {algorithm,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22746] {dataset,
## improv,
## neural,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [22747] {algorithm,
## dataset,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22748] {algorithm,
## dataset,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [22749] {algorithm,
## dataset,
## improv,
## neural} => {network} 0.1333333 1.0000000 1.578947 4
## [22750] {network,
## algorithm,
## improv,
## neural} => {dataset} 0.1333333 1.0000000 2.307692 4
## [22751] {network,
## dataset,
## improv,
## neural} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [22752] {network,
## algorithm,
## dataset,
## improv} => {neural} 0.1333333 1.0000000 3.000000 4
## [22753] {network,
## algorithm,
## dataset,
## neural} => {improv} 0.1333333 1.0000000 3.333333 4
## [22754] {algorithm,
## improv,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [22755] {network,
## algorithm,
## improv,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [22756] {network,
## improv,
## neural,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22757] {network,
## algorithm,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22758] {network,
## algorithm,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [22759] {train,
## improv,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22760] {train,
## dataset,
## improv,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [22761] {dataset,
## improv,
## neural,
## perform} => {train} 0.1000000 0.7500000 1.875000 3
## [22762] {train,
## dataset,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [22763] {train,
## dataset,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [22764] {train,
## dataset,
## improv,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [22765] {train,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22766] {dataset,
## improv,
## neural,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [22767] {train,
## dataset,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22768] {train,
## dataset,
## neural,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [22769] {train,
## dataset,
## improv,
## neural} => {network} 0.1000000 0.7500000 1.184211 3
## [22770] {network,
## train,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22771] {network,
## dataset,
## improv,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [22772] {network,
## train,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [22773] {network,
## train,
## dataset,
## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [22774] {approach,
## dataset,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [22775] {approach,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22776] {dataset,
## improv,
## neural,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [22777] {approach,
## dataset,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22778] {approach,
## dataset,
## neural,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [22779] {approach,
## dataset,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [22780] {approach,
## network,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22781] {network,
## dataset,
## improv,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [22782] {approach,
## network,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [22783] {approach,
## network,
## dataset,
## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [22784] {approach,
## improv,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [22785] {approach,
## network,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [22786] {network,
## improv,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [22787] {approach,
## network,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22788] {dataset,
## improv,
## neural,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [22789] {improv,
## neural,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22790] {dataset,
## improv,
## neural,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [22791] {dataset,
## improv,
## perform,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22792] {dataset,
## neural,
## perform,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [22793] {dataset,
## improv,
## neural,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [22794] {featur,
## improv,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22795] {featur,
## dataset,
## improv,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [22796] {featur,
## dataset,
## improv,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [22797] {featur,
## dataset,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [22798] {dataset,
## improv,
## neural,
## perform} => {network} 0.1000000 0.7500000 1.184211 3
## [22799] {network,
## improv,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22800] {network,
## dataset,
## improv,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [22801] {network,
## dataset,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [22802] {network,
## dataset,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [22803] {dataset,
## improv,
## neural,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [22804] {model,
## dataset,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [22805] {model,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22806] {model,
## dataset,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22807] {model,
## dataset,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [22808] {dataset,
## improv,
## neural,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [22809] {network,
## dataset,
## improv,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [22810] {network,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22811] {network,
## dataset,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [22812] {network,
## dataset,
## neural,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [22813] {approach,
## method,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22814] {approach,
## method,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [22815] {method,
## dataset,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [22816] {approach,
## dataset,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [22817] {approach,
## method,
## dataset,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [22818] {approach,
## method,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [22819] {approach,
## method,
## show,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [22820] {method,
## show,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [22821] {approach,
## show,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [22822] {approach,
## method,
## show,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [22823] {approach,
## method,
## dataset,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [22824] {approach,
## method,
## show,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22825] {method,
## show,
## dataset,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [22826] {approach,
## show,
## dataset,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [22827] {method,
## dataset,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [22828] {method,
## show,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22829] {method,
## show,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [22830] {show,
## dataset,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [22831] {method,
## show,
## dataset,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [22832] {train,
## algorithm,
## dataset,
## improv} => {network} 0.1000000 1.0000000 1.578947 3
## [22833] {network,
## train,
## algorithm,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22834] {network,
## algorithm,
## dataset,
## improv} => {train} 0.1000000 0.7500000 1.875000 3
## [22835] {network,
## train,
## dataset,
## improv} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22836] {network,
## train,
## algorithm,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [22837] {approach,
## algorithm,
## dataset,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [22838] {approach,
## algorithm,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22839] {algorithm,
## dataset,
## improv,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [22840] {approach,
## dataset,
## improv,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22841] {approach,
## algorithm,
## dataset,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [22842] {approach,
## algorithm,
## dataset,
## improv} => {network} 0.1000000 1.0000000 1.578947 3
## [22843] {approach,
## network,
## algorithm,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22844] {network,
## algorithm,
## dataset,
## improv} => {approach} 0.1000000 0.7500000 1.875000 3
## [22845] {approach,
## network,
## dataset,
## improv} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22846] {approach,
## network,
## algorithm,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [22847] {approach,
## algorithm,
## improv,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [22848] {approach,
## network,
## algorithm,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [22849] {network,
## algorithm,
## improv,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [22850] {approach,
## network,
## improv,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22851] {approach,
## network,
## algorithm,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [22852] {algorithm,
## dataset,
## improv,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [22853] {network,
## algorithm,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22854] {network,
## algorithm,
## dataset,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [22855] {network,
## dataset,
## improv,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22856] {network,
## algorithm,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [22857] {algorithm,
## dataset,
## improv,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [22858] {network,
## algorithm,
## dataset,
## improv} => {propos} 0.1000000 0.7500000 1.500000 3
## [22859] {network,
## algorithm,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22860] {network,
## dataset,
## improv,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [22861] {network,
## algorithm,
## dataset,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [22862] {approach,
## dataset,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [22863] {approach,
## show,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22864] {approach,
## show,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [22865] {show,
## dataset,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [22866] {approach,
## show,
## dataset,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [22867] {approach,
## dataset,
## improv,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [22868] {approach,
## network,
## dataset,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [22869] {approach,
## network,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22870] {network,
## dataset,
## improv,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [22871] {model,
## dataset,
## improv,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [22872] {featur,
## dataset,
## improv,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [22873] {featur,
## model,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22874] {featur,
## model,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [22875] {featur,
## model,
## dataset,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [22876] {algorithm,
## dataset,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22877] {network,
## algorithm,
## neural,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [22878] {network,
## dataset,
## neural,
## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [22879] {network,
## algorithm,
## dataset,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [22880] {network,
## algorithm,
## dataset,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [22881] {approach,
## train,
## neural,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [22882] {train,
## neural,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [22883] {approach,
## neural,
## propos,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [22884] {approach,
## train,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [22885] {approach,
## train,
## neural,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [22886] {approach,
## train,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22887] {network,
## train,
## neural,
## result} => {approach} 0.1000000 0.7500000 1.875000 3
## [22888] {approach,
## network,
## neural,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [22889] {approach,
## network,
## train,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [22890] {approach,
## network,
## train,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [22891] {train,
## dataset,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22892] {network,
## train,
## neural,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22893] {network,
## dataset,
## neural,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [22894] {network,
## train,
## dataset,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [22895] {network,
## train,
## dataset,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [22896] {represent,
## train,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22897] {network,
## train,
## neural,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [22898] {network,
## represent,
## neural,
## result} => {train} 0.1000000 1.0000000 2.500000 3
## [22899] {network,
## represent,
## train,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [22900] {network,
## represent,
## train,
## neural} => {result} 0.1000000 1.0000000 3.000000 3
## [22901] {train,
## neural,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22902] {network,
## train,
## neural,
## result} => {propos} 0.1000000 0.7500000 1.500000 3
## [22903] {network,
## neural,
## propos,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [22904] {network,
## train,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [22905] {network,
## train,
## neural,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [22906] {approach,
## dataset,
## neural,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [22907] {approach,
## neural,
## propos,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22908] {dataset,
## neural,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [22909] {approach,
## dataset,
## propos,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [22910] {approach,
## dataset,
## neural,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [22911] {approach,
## dataset,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22912] {approach,
## network,
## neural,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22913] {network,
## dataset,
## neural,
## result} => {approach} 0.1000000 0.7500000 1.875000 3
## [22914] {approach,
## network,
## dataset,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [22915] {approach,
## network,
## dataset,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [22916] {approach,
## neural,
## propos,
## result} => {network} 0.1333333 1.0000000 1.578947 4
## [22917] {approach,
## network,
## neural,
## result} => {propos} 0.1333333 1.0000000 2.000000 4
## [22918] {network,
## neural,
## propos,
## result} => {approach} 0.1333333 1.0000000 2.500000 4
## [22919] {approach,
## network,
## propos,
## result} => {neural} 0.1333333 1.0000000 3.000000 4
## [22920] {approach,
## network,
## neural,
## propos} => {result} 0.1333333 0.8000000 2.400000 4
## [22921] {dataset,
## neural,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22922] {network,
## dataset,
## neural,
## result} => {propos} 0.1000000 0.7500000 1.500000 3
## [22923] {network,
## neural,
## propos,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22924] {network,
## dataset,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [22925] {network,
## dataset,
## neural,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [22926] {task,
## train,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22927] {represent,
## task,
## train,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22928] {represent,
## train,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22929] {represent,
## task,
## train,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [22930] {task,
## train,
## result,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [22931] {network,
## task,
## train,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22932] {network,
## task,
## result,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [22933] {network,
## train,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22934] {network,
## task,
## train,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [22935] {represent,
## task,
## train,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [22936] {network,
## task,
## train,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [22937] {network,
## represent,
## task,
## result} => {train} 0.1000000 1.0000000 2.500000 3
## [22938] {network,
## represent,
## train,
## result} => {task} 0.1000000 0.7500000 2.045455 3
## [22939] {network,
## represent,
## task,
## train} => {result} 0.1000000 0.7500000 2.250000 3
## [22940] {approach,
## task,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22941] {approach,
## represent,
## task,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22942] {approach,
## represent,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22943] {approach,
## represent,
## task,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [22944] {approach,
## task,
## result,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [22945] {approach,
## task,
## propos,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22946] {task,
## propos,
## result,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [22947] {approach,
## propos,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22948] {approach,
## task,
## propos,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [22949] {approach,
## represent,
## task,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [22950] {approach,
## task,
## propos,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [22951] {represent,
## task,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [22952] {approach,
## represent,
## propos,
## result} => {task} 0.1000000 1.0000000 2.727273 3
## [22953] {data,
## task,
## dataset,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22954] {data,
## task,
## result,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22955] {task,
## dataset,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22956] {data,
## dataset,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22957] {data,
## task,
## dataset,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [22958] {data,
## represent,
## task,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [22959] {represent,
## task,
## dataset,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [22960] {data,
## represent,
## dataset,
## result} => {task} 0.1000000 0.7500000 2.045455 3
## [22961] {data,
## represent,
## task,
## dataset} => {result} 0.1000000 0.7500000 2.250000 3
## [22962] {data,
## task,
## result,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [22963] {data,
## represent,
## task,
## result} => {learn} 0.1333333 1.0000000 2.307692 4
## [22964] {represent,
## task,
## result,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [22965] {data,
## represent,
## result,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [22966] {data,
## represent,
## task,
## learn} => {result} 0.1333333 0.8000000 2.400000 4
## [22967] {data,
## task,
## result,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [22968] {data,
## show,
## task,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22969] {show,
## task,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [22970] {data,
## show,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22971] {data,
## task,
## result,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [22972] {data,
## featur,
## task,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22973] {featur,
## task,
## result,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [22974] {data,
## featur,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22975] {data,
## represent,
## task,
## result} => {show} 0.1000000 0.7500000 1.406250 3
## [22976] {data,
## show,
## task,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [22977] {represent,
## show,
## task,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [22978] {data,
## represent,
## show,
## result} => {task} 0.1000000 1.0000000 2.727273 3
## [22979] {data,
## represent,
## task,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [22980] {data,
## featur,
## task,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [22981] {featur,
## represent,
## task,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [22982] {data,
## featur,
## represent,
## result} => {task} 0.1000000 0.7500000 2.045455 3
## [22983] {task,
## dataset,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22984] {represent,
## task,
## dataset,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22985] {represent,
## dataset,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22986] {represent,
## task,
## dataset,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [22987] {show,
## task,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22988] {represent,
## show,
## task,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22989] {represent,
## show,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22990] {represent,
## show,
## task,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [22991] {task,
## propos,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [22992] {represent,
## task,
## propos,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [22993] {represent,
## propos,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [22994] {represent,
## task,
## propos,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [22995] {represent,
## task,
## result,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [22996] {featur,
## task,
## result,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [22997] {featur,
## represent,
## task,
## result} => {learn} 0.1333333 1.0000000 2.307692 4
## [22998] {featur,
## represent,
## result,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [22999] {featur,
## represent,
## task,
## learn} => {result} 0.1333333 0.8000000 2.400000 4
## [23000] {network,
## task,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23001] {network,
## represent,
## task,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [23002] {network,
## represent,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23003] {network,
## represent,
## task,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [23004] {approach,
## train,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [23005] {approach,
## network,
## train,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [23006] {network,
## train,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [23007] {approach,
## network,
## propos,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [23008] {approach,
## network,
## train,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [23009] {data,
## train,
## dataset,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [23010] {data,
## represent,
## train,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23011] {represent,
## train,
## dataset,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [23012] {data,
## represent,
## dataset,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [23013] {data,
## represent,
## train,
## dataset} => {result} 0.1000000 0.7500000 2.250000 3
## [23014] {data,
## train,
## dataset,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [23015] {data,
## network,
## train,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23016] {network,
## train,
## dataset,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [23017] {data,
## network,
## dataset,
## result} => {train} 0.1000000 1.0000000 2.500000 3
## [23018] {data,
## network,
## train,
## dataset} => {result} 0.1000000 1.0000000 3.000000 3
## [23019] {data,
## represent,
## train,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [23020] {data,
## network,
## train,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [23021] {network,
## represent,
## train,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [23022] {data,
## network,
## represent,
## result} => {train} 0.1000000 1.0000000 2.500000 3
## [23023] {data,
## network,
## represent,
## train} => {result} 0.1000000 0.7500000 2.250000 3
## [23024] {represent,
## train,
## dataset,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [23025] {network,
## train,
## dataset,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [23026] {network,
## represent,
## train,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23027] {network,
## represent,
## dataset,
## result} => {train} 0.1000000 1.0000000 2.500000 3
## [23028] {network,
## represent,
## train,
## dataset} => {result} 0.1000000 1.0000000 3.000000 3
## [23029] {represent,
## train,
## result,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [23030] {network,
## train,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23031] {network,
## represent,
## train,
## result} => {learn} 0.1000000 0.7500000 1.730769 3
## [23032] {network,
## represent,
## result,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [23033] {network,
## represent,
## train,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [23034] {featur,
## represent,
## train,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [23035] {network,
## represent,
## train,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [23036] {featur,
## network,
## train,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [23037] {featur,
## network,
## represent,
## result} => {train} 0.1000000 1.0000000 2.500000 3
## [23038] {featur,
## network,
## represent,
## train} => {result} 0.1000000 0.7500000 2.250000 3
## [23039] {approach,
## dataset,
## propos,
## result} => {model} 0.1000000 0.7500000 1.406250 3
## [23040] {approach,
## model,
## dataset,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [23041] {approach,
## model,
## propos,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23042] {model,
## dataset,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [23043] {approach,
## dataset,
## propos,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [23044] {approach,
## network,
## dataset,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [23045] {approach,
## network,
## propos,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23046] {network,
## dataset,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [23047] {approach,
## represent,
## result,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23048] {approach,
## propos,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23049] {approach,
## represent,
## propos,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [23050] {represent,
## propos,
## result,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [23051] {data,
## dataset,
## result,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [23052] {data,
## represent,
## result,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23053] {represent,
## dataset,
## result,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23054] {data,
## represent,
## dataset,
## result} => {work} 0.1000000 0.7500000 1.875000 3
## [23055] {data,
## represent,
## dataset,
## work} => {result} 0.1000000 0.7500000 2.250000 3
## [23056] {data,
## dataset,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23057] {data,
## represent,
## dataset,
## result} => {learn} 0.1000000 0.7500000 1.730769 3
## [23058] {data,
## represent,
## result,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23059] {represent,
## dataset,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23060] {data,
## represent,
## dataset,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [23061] {data,
## featur,
## dataset,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [23062] {data,
## featur,
## represent,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23063] {featur,
## represent,
## dataset,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [23064] {data,
## represent,
## dataset,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [23065] {data,
## network,
## dataset,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [23066] {data,
## network,
## represent,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23067] {network,
## represent,
## dataset,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [23068] {data,
## network,
## represent,
## dataset} => {result} 0.1000000 0.7500000 2.250000 3
## [23069] {data,
## represent,
## result,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [23070] {data,
## show,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23071] {data,
## represent,
## show,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [23072] {represent,
## show,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23073] {data,
## represent,
## show,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [23074] {data,
## represent,
## result,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23075] {data,
## featur,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23076] {data,
## featur,
## represent,
## result} => {learn} 0.1000000 0.7500000 1.730769 3
## [23077] {featur,
## represent,
## result,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [23078] {approach,
## method,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [23079] {approach,
## method,
## network,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [23080] {method,
## network,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [23081] {approach,
## method,
## network,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [23082] {train,
## algorithm,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [23083] {network,
## train,
## algorithm,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23084] {network,
## algorithm,
## dataset,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [23085] {network,
## train,
## dataset,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [23086] {network,
## train,
## algorithm,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [23087] {approach,
## algorithm,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [23088] {approach,
## algorithm,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23089] {algorithm,
## dataset,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [23090] {approach,
## dataset,
## neural,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [23091] {approach,
## algorithm,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [23092] {approach,
## algorithm,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [23093] {approach,
## network,
## algorithm,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23094] {network,
## algorithm,
## dataset,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [23095] {approach,
## network,
## dataset,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [23096] {approach,
## network,
## algorithm,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [23097] {approach,
## show,
## algorithm,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [23098] {approach,
## network,
## algorithm,
## neural} => {show} 0.1000000 0.7500000 1.406250 3
## [23099] {network,
## show,
## algorithm,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [23100] {approach,
## network,
## show,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [23101] {approach,
## network,
## show,
## algorithm} => {neural} 0.1000000 1.0000000 3.000000 3
## [23102] {approach,
## algorithm,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [23103] {approach,
## network,
## algorithm,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [23104] {network,
## algorithm,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [23105] {approach,
## network,
## algorithm,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [23106] {approach,
## model,
## algorithm,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [23107] {approach,
## network,
## algorithm,
## neural} => {model} 0.1000000 0.7500000 1.406250 3
## [23108] {model,
## network,
## algorithm,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [23109] {approach,
## model,
## network,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [23110] {approach,
## model,
## network,
## algorithm} => {neural} 0.1000000 1.0000000 3.000000 3
## [23111] {algorithm,
## dataset,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [23112] {network,
## algorithm,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23113] {network,
## algorithm,
## dataset,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [23114] {network,
## dataset,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [23115] {network,
## algorithm,
## dataset,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [23116] {algorithm,
## dataset,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [23117] {network,
## algorithm,
## dataset,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [23118] {network,
## algorithm,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23119] {network,
## dataset,
## neural,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [23120] {network,
## algorithm,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [23121] {approach,
## train,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [23122] {approach,
## train,
## neural,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23123] {train,
## dataset,
## neural,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [23124] {approach,
## dataset,
## neural,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [23125] {approach,
## train,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [23126] {approach,
## train,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [23127] {approach,
## network,
## train,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23128] {network,
## train,
## dataset,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [23129] {approach,
## network,
## dataset,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [23130] {approach,
## network,
## train,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [23131] {approach,
## train,
## neural,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [23132] {approach,
## network,
## train,
## neural} => {propos} 0.1333333 1.0000000 2.000000 4
## [23133] {network,
## train,
## neural,
## propos} => {approach} 0.1333333 1.0000000 2.500000 4
## [23134] {approach,
## network,
## neural,
## propos} => {train} 0.1333333 0.8000000 2.000000 4
## [23135] {approach,
## network,
## train,
## propos} => {neural} 0.1333333 1.0000000 3.000000 4
## [23136] {train,
## dataset,
## neural,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [23137] {network,
## train,
## neural,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23138] {network,
## train,
## dataset,
## neural} => {work} 0.1000000 0.7500000 1.875000 3
## [23139] {network,
## dataset,
## neural,
## work} => {train} 0.1000000 1.0000000 2.500000 3
## [23140] {network,
## train,
## dataset,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [23141] {data,
## train,
## dataset,
## neural} => {represent} 0.1000000 1.0000000 2.000000 3
## [23142] {data,
## represent,
## train,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23143] {represent,
## train,
## dataset,
## neural} => {data} 0.1000000 1.0000000 2.307692 3
## [23144] {data,
## represent,
## dataset,
## neural} => {train} 0.1000000 1.0000000 2.500000 3
## [23145] {data,
## represent,
## train,
## dataset} => {neural} 0.1000000 0.7500000 2.250000 3
## [23146] {train,
## dataset,
## neural,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [23147] {network,
## train,
## dataset,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [23148] {network,
## train,
## neural,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23149] {network,
## dataset,
## neural,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [23150] {network,
## train,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [23151] {represent,
## train,
## neural,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23152] {train,
## neural,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23153] {represent,
## train,
## neural,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23154] {represent,
## neural,
## propos,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [23155] {represent,
## train,
## propos,
## learn} => {neural} 0.1000000 1.0000000 3.000000 3
## [23156] {approach,
## show,
## neural,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [23157] {approach,
## network,
## neural,
## work} => {show} 0.1000000 1.0000000 1.875000 3
## [23158] {approach,
## network,
## show,
## neural} => {work} 0.1000000 0.7500000 1.875000 3
## [23159] {network,
## show,
## neural,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [23160] {approach,
## network,
## show,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [23161] {approach,
## show,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [23162] {approach,
## dataset,
## neural,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [23163] {approach,
## show,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23164] {show,
## dataset,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [23165] {approach,
## show,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [23166] {approach,
## network,
## dataset,
## neural} => {show} 0.1000000 0.7500000 1.406250 3
## [23167] {approach,
## network,
## show,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23168] {network,
## show,
## dataset,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [23169] {approach,
## network,
## show,
## dataset} => {neural} 0.1000000 0.7500000 2.250000 3
## [23170] {approach,
## dataset,
## neural,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [23171] {approach,
## network,
## dataset,
## neural} => {propos} 0.1333333 1.0000000 2.000000 4
## [23172] {approach,
## network,
## neural,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [23173] {network,
## dataset,
## neural,
## propos} => {approach} 0.1333333 1.0000000 2.500000 4
## [23174] {approach,
## network,
## dataset,
## propos} => {neural} 0.1333333 0.8000000 2.400000 4
## [23175] {approach,
## represent,
## neural,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [23176] {approach,
## network,
## neural,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23177] {approach,
## network,
## represent,
## neural} => {learn} 0.1000000 1.0000000 2.307692 3
## [23178] {network,
## represent,
## neural,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [23179] {approach,
## network,
## represent,
## learn} => {neural} 0.1000000 0.7500000 2.250000 3
## [23180] {approach,
## show,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [23181] {approach,
## network,
## show,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [23182] {network,
## show,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [23183] {approach,
## network,
## show,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [23184] {show,
## dataset,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [23185] {network,
## show,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [23186] {network,
## dataset,
## neural,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [23187] {network,
## show,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23188] {network,
## show,
## dataset,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [23189] {approach,
## method,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [23190] {approach,
## featur,
## method,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [23191] {featur,
## method,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [23192] {approach,
## featur,
## method,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [23193] {approach,
## method,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [23194] {approach,
## method,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [23195] {method,
## network,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [23196] {approach,
## method,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [23197] {approach,
## network,
## represent,
## task} => {method} 0.1000000 0.7500000 2.045455 3
## [23198] {approach,
## featur,
## method,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [23199] {approach,
## method,
## network,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [23200] {featur,
## method,
## network,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [23201] {approach,
## featur,
## method,
## network} => {task} 0.1000000 0.7500000 2.045455 3
## [23202] {approach,
## featur,
## network,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [23203] {featur,
## method,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [23204] {method,
## network,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [23205] {featur,
## method,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [23206] {featur,
## method,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [23207] {featur,
## network,
## represent,
## task} => {method} 0.1000000 0.7500000 2.045455 3
## [23208] {approach,
## method,
## dataset,
## perform} => {show} 0.1333333 1.0000000 1.875000 4
## [23209] {approach,
## method,
## show,
## perform} => {dataset} 0.1333333 1.0000000 2.307692 4
## [23210] {approach,
## method,
## show,
## dataset} => {perform} 0.1333333 0.8000000 1.714286 4
## [23211] {method,
## show,
## dataset,
## perform} => {approach} 0.1333333 1.0000000 2.500000 4
## [23212] {approach,
## show,
## dataset,
## perform} => {method} 0.1333333 1.0000000 2.727273 4
## [23213] {approach,
## method,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [23214] {approach,
## method,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23215] {approach,
## method,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [23216] {method,
## dataset,
## perform,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [23217] {approach,
## dataset,
## perform,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [23218] {approach,
## method,
## dataset,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [23219] {approach,
## method,
## model,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23220] {approach,
## method,
## model,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [23221] {method,
## model,
## dataset,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [23222] {approach,
## model,
## dataset,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [23223] {approach,
## method,
## show,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [23224] {approach,
## method,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [23225] {approach,
## method,
## show,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [23226] {method,
## show,
## perform,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [23227] {approach,
## show,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [23228] {approach,
## method,
## show,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [23229] {approach,
## method,
## model,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [23230] {method,
## model,
## show,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [23231] {approach,
## model,
## show,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [23232] {approach,
## method,
## dataset,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [23233] {approach,
## method,
## show,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23234] {method,
## show,
## dataset,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [23235] {approach,
## show,
## dataset,
## learn} => {method} 0.1000000 0.7500000 2.045455 3
## [23236] {approach,
## method,
## dataset,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [23237] {approach,
## method,
## model,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [23238] {approach,
## method,
## model,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23239] {method,
## model,
## dataset,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [23240] {approach,
## model,
## dataset,
## learn} => {method} 0.1000000 0.7500000 2.045455 3
## [23241] {approach,
## method,
## show,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [23242] {approach,
## method,
## dataset,
## propos} => {show} 0.1333333 1.0000000 1.875000 4
## [23243] {approach,
## method,
## show,
## propos} => {dataset} 0.1333333 1.0000000 2.307692 4
## [23244] {method,
## show,
## dataset,
## propos} => {approach} 0.1333333 1.0000000 2.500000 4
## [23245] {approach,
## show,
## dataset,
## propos} => {method} 0.1333333 0.8000000 2.181818 4
## [23246] {approach,
## method,
## show,
## dataset} => {model} 0.1333333 0.8000000 1.500000 4
## [23247] {approach,
## method,
## model,
## dataset} => {show} 0.1333333 1.0000000 1.875000 4
## [23248] {approach,
## method,
## model,
## show} => {dataset} 0.1333333 0.8000000 1.846154 4
## [23249] {method,
## model,
## show,
## dataset} => {approach} 0.1333333 1.0000000 2.500000 4
## [23250] {approach,
## model,
## show,
## dataset} => {method} 0.1333333 1.0000000 2.727273 4
## [23251] {approach,
## featur,
## method,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [23252] {approach,
## featur,
## method,
## show} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23253] {featur,
## method,
## show,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [23254] {approach,
## featur,
## show,
## dataset} => {method} 0.1000000 1.0000000 2.727273 3
## [23255] {approach,
## method,
## network,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [23256] {approach,
## method,
## network,
## show} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23257] {method,
## network,
## show,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [23258] {approach,
## network,
## show,
## dataset} => {method} 0.1000000 0.7500000 2.045455 3
## [23259] {approach,
## method,
## dataset,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [23260] {approach,
## method,
## model,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [23261] {approach,
## method,
## model,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23262] {method,
## model,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [23263] {approach,
## method,
## dataset,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [23264] {approach,
## method,
## network,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [23265] {approach,
## method,
## network,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23266] {method,
## network,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [23267] {approach,
## method,
## model,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [23268] {approach,
## featur,
## method,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [23269] {approach,
## featur,
## method,
## model} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23270] {featur,
## method,
## model,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [23271] {approach,
## featur,
## model,
## dataset} => {method} 0.1000000 0.7500000 2.045455 3
## [23272] {approach,
## method,
## represent,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23273] {approach,
## method,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23274] {approach,
## method,
## represent,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23275] {method,
## represent,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [23276] {approach,
## method,
## show,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [23277] {approach,
## method,
## model,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [23278] {method,
## model,
## show,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [23279] {approach,
## model,
## show,
## learn} => {method} 0.1000000 0.7500000 2.045455 3
## [23280] {approach,
## method,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [23281] {approach,
## method,
## model,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [23282] {method,
## model,
## represent,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [23283] {approach,
## model,
## represent,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [23284] {approach,
## featur,
## method,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [23285] {approach,
## method,
## network,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [23286] {approach,
## featur,
## method,
## network} => {represent} 0.1000000 0.7500000 1.500000 3
## [23287] {featur,
## method,
## network,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [23288] {approach,
## featur,
## network,
## represent} => {method} 0.1000000 0.7500000 2.045455 3
## [23289] {approach,
## method,
## show,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [23290] {approach,
## method,
## model,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [23291] {method,
## model,
## show,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [23292] {approach,
## model,
## show,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [23293] {approach,
## method,
## show,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [23294] {approach,
## method,
## network,
## show} => {propos} 0.1000000 0.7500000 1.500000 3
## [23295] {approach,
## method,
## network,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [23296] {method,
## network,
## show,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [23297] {approach,
## network,
## show,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [23298] {approach,
## method,
## model,
## show} => {featur} 0.1333333 0.8000000 1.500000 4
## [23299] {approach,
## featur,
## method,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [23300] {approach,
## featur,
## method,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [23301] {featur,
## method,
## model,
## show} => {approach} 0.1333333 1.0000000 2.500000 4
## [23302] {approach,
## featur,
## model,
## show} => {method} 0.1333333 0.8000000 2.181818 4
## [23303] {approach,
## method,
## network,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [23304] {approach,
## method,
## model,
## network} => {show} 0.1000000 1.0000000 1.875000 3
## [23305] {method,
## model,
## network,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [23306] {approach,
## model,
## network,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [23307] {approach,
## featur,
## method,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [23308] {approach,
## method,
## network,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [23309] {approach,
## featur,
## method,
## network} => {show} 0.1000000 0.7500000 1.406250 3
## [23310] {featur,
## method,
## network,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [23311] {approach,
## featur,
## network,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [23312] {approach,
## featur,
## method,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [23313] {approach,
## method,
## network,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [23314] {approach,
## featur,
## method,
## network} => {propos} 0.1000000 0.7500000 1.500000 3
## [23315] {featur,
## method,
## network,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [23316] {approach,
## featur,
## network,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [23317] {approach,
## featur,
## method,
## model} => {network} 0.1000000 0.7500000 1.184211 3
## [23318] {approach,
## method,
## model,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [23319] {approach,
## featur,
## method,
## network} => {model} 0.1000000 0.7500000 1.406250 3
## [23320] {featur,
## method,
## model,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [23321] {approach,
## featur,
## model,
## network} => {method} 0.1000000 0.7500000 2.045455 3
## [23322] {method,
## dataset,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [23323] {method,
## network,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23324] {method,
## network,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23325] {method,
## network,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [23326] {method,
## show,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [23327] {method,
## dataset,
## perform,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [23328] {method,
## show,
## perform,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23329] {method,
## show,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [23330] {show,
## dataset,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [23331] {method,
## show,
## dataset,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [23332] {method,
## model,
## dataset,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [23333] {method,
## model,
## show,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23334] {method,
## model,
## show,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [23335] {model,
## show,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [23336] {method,
## dataset,
## perform,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [23337] {method,
## network,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [23338] {method,
## network,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23339] {method,
## network,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [23340] {network,
## dataset,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [23341] {method,
## show,
## dataset,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [23342] {method,
## model,
## dataset,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [23343] {method,
## model,
## show,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [23344] {method,
## model,
## show,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23345] {model,
## show,
## dataset,
## learn} => {method} 0.1000000 1.0000000 2.727273 3
## [23346] {method,
## show,
## dataset,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [23347] {method,
## model,
## show,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [23348] {method,
## model,
## dataset,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [23349] {method,
## model,
## show,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23350] {model,
## show,
## dataset,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [23351] {method,
## show,
## dataset,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [23352] {method,
## network,
## show,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [23353] {method,
## network,
## dataset,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [23354] {method,
## network,
## show,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23355] {network,
## show,
## dataset,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [23356] {method,
## model,
## show,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [23357] {featur,
## method,
## show,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [23358] {featur,
## method,
## model,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [23359] {featur,
## method,
## model,
## show} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23360] {featur,
## model,
## show,
## dataset} => {method} 0.1000000 1.0000000 2.727273 3
## [23361] {featur,
## method,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [23362] {method,
## network,
## dataset,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [23363] {featur,
## method,
## network,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [23364] {featur,
## method,
## network,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23365] {featur,
## network,
## dataset,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [23366] {method,
## represent,
## show,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23367] {method,
## represent,
## propos,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [23368] {method,
## show,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23369] {method,
## represent,
## show,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23370] {represent,
## show,
## propos,
## learn} => {method} 0.1000000 0.7500000 2.045455 3
## [23371] {method,
## represent,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23372] {featur,
## method,
## represent,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23373] {featur,
## method,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23374] {featur,
## method,
## represent,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23375] {featur,
## method,
## model,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [23376] {method,
## model,
## network,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [23377] {featur,
## method,
## network,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [23378] {featur,
## method,
## model,
## network} => {show} 0.1000000 1.0000000 1.875000 3
## [23379] {featur,
## model,
## network,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [23380] {approach,
## algorithm,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [23381] {approach,
## network,
## algorithm,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [23382] {approach,
## network,
## algorithm,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23383] {network,
## algorithm,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [23384] {model,
## show,
## algorithm,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [23385] {featur,
## show,
## algorithm,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [23386] {featur,
## model,
## algorithm,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [23387] {featur,
## model,
## show,
## algorithm} => {learn} 0.1000000 0.7500000 1.730769 3
## [23388] {approach,
## represent,
## task,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [23389] {approach,
## network,
## task,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [23390] {network,
## represent,
## task,
## train} => {approach} 0.1000000 0.7500000 1.875000 3
## [23391] {approach,
## network,
## represent,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [23392] {approach,
## network,
## represent,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [23393] {data,
## task,
## train,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [23394] {data,
## show,
## task,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [23395] {show,
## task,
## train,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23396] {data,
## show,
## train,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23397] {data,
## represent,
## task,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [23398] {data,
## show,
## task,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [23399] {represent,
## show,
## task,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [23400] {data,
## represent,
## show,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [23401] {data,
## represent,
## task,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [23402] {data,
## network,
## task,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [23403] {network,
## represent,
## task,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [23404] {data,
## network,
## represent,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [23405] {data,
## network,
## represent,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [23406] {data,
## show,
## task,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [23407] {data,
## featur,
## task,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [23408] {featur,
## show,
## task,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [23409] {data,
## featur,
## show,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [23410] {data,
## show,
## task,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [23411] {data,
## network,
## task,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [23412] {network,
## show,
## task,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [23413] {data,
## network,
## show,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [23414] {data,
## network,
## show,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [23415] {represent,
## task,
## train,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [23416] {network,
## task,
## train,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23417] {network,
## represent,
## task,
## train} => {learn} 0.1000000 0.7500000 1.730769 3
## [23418] {network,
## represent,
## task,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [23419] {network,
## represent,
## train,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23420] {represent,
## show,
## task,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [23421] {network,
## represent,
## task,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [23422] {network,
## show,
## task,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [23423] {network,
## represent,
## show,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [23424] {network,
## represent,
## show,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [23425] {featur,
## represent,
## task,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [23426] {network,
## represent,
## task,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [23427] {featur,
## network,
## task,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [23428] {featur,
## network,
## represent,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [23429] {featur,
## network,
## represent,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [23430] {approach,
## data,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23431] {approach,
## task,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23432] {approach,
## data,
## task,
## dataset} => {work} 0.1000000 1.0000000 2.500000 3
## [23433] {data,
## task,
## dataset,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [23434] {approach,
## data,
## dataset,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23435] {approach,
## data,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23436] {approach,
## task,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23437] {approach,
## data,
## task,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [23438] {data,
## task,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [23439] {approach,
## data,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23440] {approach,
## data,
## task,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [23441] {approach,
## represent,
## task,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23442] {data,
## represent,
## task,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [23443] {approach,
## data,
## represent,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23444] {approach,
## data,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23445] {approach,
## task,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23446] {approach,
## data,
## task,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [23447] {data,
## task,
## propos,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [23448] {approach,
## data,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23449] {approach,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23450] {approach,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23451] {approach,
## task,
## dataset,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [23452] {task,
## dataset,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [23453] {approach,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23454] {approach,
## task,
## dataset,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [23455] {approach,
## represent,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23456] {approach,
## represent,
## task,
## dataset} => {work} 0.1000000 1.0000000 2.500000 3
## [23457] {represent,
## task,
## dataset,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [23458] {approach,
## represent,
## dataset,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23459] {approach,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23460] {approach,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23461] {approach,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [23462] {task,
## dataset,
## propos,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [23463] {approach,
## dataset,
## propos,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [23464] {approach,
## task,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23465] {approach,
## represent,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23466] {approach,
## represent,
## task,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [23467] {represent,
## task,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [23468] {approach,
## represent,
## work,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23469] {approach,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23470] {approach,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23471] {approach,
## task,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [23472] {task,
## propos,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [23473] {approach,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23474] {approach,
## represent,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23475] {approach,
## task,
## propos,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [23476] {represent,
## task,
## propos,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [23477] {approach,
## represent,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23478] {approach,
## data,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [23479] {approach,
## data,
## task,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23480] {approach,
## task,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23481] {approach,
## data,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23482] {approach,
## data,
## task,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [23483] {approach,
## represent,
## task,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [23484] {data,
## represent,
## task,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [23485] {approach,
## data,
## represent,
## dataset} => {task} 0.1000000 1.0000000 2.727273 3
## [23486] {approach,
## data,
## task,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [23487] {approach,
## data,
## task,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23488] {approach,
## task,
## dataset,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [23489] {data,
## task,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [23490] {approach,
## data,
## dataset,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [23491] {approach,
## data,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23492] {approach,
## represent,
## task,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [23493] {approach,
## data,
## represent,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23494] {approach,
## data,
## task,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23495] {approach,
## data,
## task,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [23496] {approach,
## task,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [23497] {approach,
## data,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23498] {approach,
## data,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [23499] {approach,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [23500] {approach,
## data,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [23501] {approach,
## data,
## represent,
## task} => {propos} 0.1333333 0.8000000 1.600000 4
## [23502] {approach,
## data,
## task,
## propos} => {represent} 0.1333333 1.0000000 2.000000 4
## [23503] {approach,
## represent,
## task,
## propos} => {data} 0.1333333 0.8000000 1.846154 4
## [23504] {data,
## represent,
## task,
## propos} => {approach} 0.1333333 1.0000000 2.500000 4
## [23505] {approach,
## data,
## represent,
## propos} => {task} 0.1333333 1.0000000 2.727273 4
## [23506] {approach,
## data,
## model,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [23507] {approach,
## model,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [23508] {data,
## model,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [23509] {approach,
## data,
## model,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [23510] {approach,
## data,
## represent,
## task} => {featur} 0.1333333 0.8000000 1.500000 4
## [23511] {approach,
## data,
## featur,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [23512] {approach,
## featur,
## represent,
## task} => {data} 0.1333333 0.8000000 1.846154 4
## [23513] {approach,
## data,
## featur,
## represent} => {task} 0.1333333 1.0000000 2.727273 4
## [23514] {approach,
## data,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [23515] {approach,
## network,
## represent,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [23516] {data,
## network,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [23517] {approach,
## data,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [23518] {approach,
## data,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [23519] {approach,
## data,
## network,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [23520] {approach,
## network,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [23521] {data,
## network,
## show,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [23522] {approach,
## data,
## network,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [23523] {approach,
## data,
## task,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [23524] {approach,
## data,
## featur,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [23525] {approach,
## featur,
## task,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [23526] {approach,
## data,
## featur,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [23527] {approach,
## data,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [23528] {approach,
## data,
## featur,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [23529] {approach,
## featur,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [23530] {approach,
## data,
## featur,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [23531] {approach,
## task,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23532] {approach,
## represent,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [23533] {approach,
## represent,
## task,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23534] {represent,
## task,
## dataset,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [23535] {approach,
## represent,
## dataset,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23536] {approach,
## task,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23537] {approach,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23538] {approach,
## task,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23539] {task,
## dataset,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [23540] {approach,
## dataset,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23541] {approach,
## represent,
## task,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [23542] {approach,
## task,
## dataset,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [23543] {represent,
## task,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [23544] {approach,
## represent,
## dataset,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [23545] {approach,
## represent,
## task,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [23546] {approach,
## task,
## propos,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [23547] {approach,
## represent,
## task,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [23548] {represent,
## task,
## propos,
## learn} => {approach} 0.1333333 1.0000000 2.500000 4
## [23549] {approach,
## represent,
## propos,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [23550] {approach,
## represent,
## task,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23551] {approach,
## featur,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23552] {approach,
## featur,
## represent,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23553] {approach,
## represent,
## task,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [23554] {approach,
## network,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23555] {approach,
## network,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [23556] {network,
## represent,
## task,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [23557] {approach,
## network,
## represent,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23558] {approach,
## task,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23559] {approach,
## featur,
## task,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23560] {approach,
## featur,
## task,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [23561] {approach,
## featur,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23562] {approach,
## task,
## propos,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [23563] {approach,
## network,
## task,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23564] {approach,
## network,
## task,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23565] {network,
## task,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [23566] {approach,
## network,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23567] {approach,
## represent,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [23568] {approach,
## network,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [23569] {approach,
## network,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [23570] {network,
## represent,
## show,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [23571] {approach,
## network,
## represent,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [23572] {approach,
## represent,
## task,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [23573] {approach,
## featur,
## represent,
## task} => {propos} 0.1333333 0.8000000 1.600000 4
## [23574] {approach,
## featur,
## task,
## propos} => {represent} 0.1333333 1.0000000 2.000000 4
## [23575] {featur,
## represent,
## task,
## propos} => {approach} 0.1333333 1.0000000 2.500000 4
## [23576] {approach,
## featur,
## represent,
## propos} => {task} 0.1333333 1.0000000 2.727273 4
## [23577] {approach,
## network,
## represent,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [23578] {approach,
## network,
## task,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [23579] {network,
## represent,
## task,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [23580] {approach,
## network,
## represent,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [23581] {approach,
## model,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [23582] {approach,
## featur,
## model,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [23583] {featur,
## model,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [23584] {approach,
## featur,
## model,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [23585] {approach,
## network,
## represent,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [23586] {approach,
## featur,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [23587] {featur,
## network,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [23588] {approach,
## featur,
## network,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [23589] {data,
## task,
## dataset,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [23590] {data,
## task,
## work,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [23591] {task,
## dataset,
## work,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [23592] {data,
## task,
## dataset,
## learn} => {work} 0.1333333 0.8000000 2.000000 4
## [23593] {data,
## dataset,
## work,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [23594] {data,
## task,
## dataset,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [23595] {data,
## represent,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23596] {represent,
## task,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23597] {data,
## represent,
## task,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [23598] {data,
## represent,
## dataset,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [23599] {data,
## task,
## dataset,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [23600] {data,
## task,
## propos,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [23601] {task,
## dataset,
## propos,
## work} => {data} 0.1333333 1.0000000 2.307692 4
## [23602] {data,
## task,
## dataset,
## propos} => {work} 0.1333333 1.0000000 2.500000 4
## [23603] {data,
## dataset,
## propos,
## work} => {task} 0.1333333 1.0000000 2.727273 4
## [23604] {data,
## task,
## dataset,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [23605] {data,
## model,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23606] {model,
## task,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23607] {data,
## model,
## task,
## dataset} => {work} 0.1000000 1.0000000 2.500000 3
## [23608] {data,
## model,
## dataset,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23609] {data,
## task,
## dataset,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [23610] {data,
## featur,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23611] {featur,
## task,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23612] {data,
## featur,
## task,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [23613] {data,
## featur,
## dataset,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [23614] {data,
## task,
## dataset,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [23615] {data,
## network,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23616] {network,
## task,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23617] {data,
## network,
## task,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [23618] {data,
## network,
## dataset,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [23619] {data,
## task,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [23620] {data,
## represent,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23621] {represent,
## task,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23622] {data,
## represent,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23623] {data,
## task,
## work,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [23624] {data,
## task,
## propos,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [23625] {task,
## propos,
## work,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [23626] {data,
## task,
## propos,
## learn} => {work} 0.1333333 0.8000000 2.000000 4
## [23627] {data,
## propos,
## work,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [23628] {data,
## task,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [23629] {data,
## model,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23630] {model,
## task,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23631] {data,
## model,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23632] {data,
## task,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23633] {data,
## featur,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23634] {featur,
## task,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23635] {data,
## featur,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23636] {data,
## task,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [23637] {data,
## network,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23638] {network,
## task,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23639] {data,
## network,
## task,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [23640] {data,
## network,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23641] {data,
## represent,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23642] {data,
## task,
## propos,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [23643] {represent,
## task,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23644] {data,
## represent,
## task,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [23645] {data,
## represent,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23646] {data,
## task,
## propos,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [23647] {data,
## model,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23648] {model,
## task,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23649] {data,
## model,
## task,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [23650] {data,
## model,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23651] {data,
## task,
## propos,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [23652] {data,
## featur,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23653] {featur,
## task,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23654] {data,
## featur,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23655] {data,
## task,
## propos,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [23656] {data,
## network,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23657] {network,
## task,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23658] {data,
## network,
## task,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [23659] {data,
## network,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23660] {data,
## model,
## task,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [23661] {data,
## featur,
## task,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [23662] {featur,
## model,
## task,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [23663] {data,
## featur,
## model,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23664] {task,
## dataset,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [23665] {represent,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23666] {represent,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23667] {represent,
## task,
## dataset,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [23668] {represent,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23669] {task,
## dataset,
## work,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [23670] {task,
## dataset,
## propos,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [23671] {task,
## propos,
## work,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [23672] {task,
## dataset,
## propos,
## learn} => {work} 0.1333333 1.0000000 2.500000 4
## [23673] {dataset,
## propos,
## work,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [23674] {task,
## dataset,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [23675] {model,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23676] {model,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23677] {model,
## task,
## dataset,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [23678] {model,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23679] {task,
## dataset,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23680] {featur,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23681] {featur,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23682] {featur,
## task,
## dataset,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [23683] {featur,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23684] {task,
## dataset,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [23685] {network,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23686] {network,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23687] {network,
## task,
## dataset,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [23688] {network,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23689] {represent,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23690] {task,
## dataset,
## propos,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [23691] {represent,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23692] {represent,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [23693] {represent,
## dataset,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23694] {task,
## dataset,
## propos,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [23695] {model,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23696] {model,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23697] {model,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [23698] {model,
## dataset,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23699] {task,
## dataset,
## propos,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [23700] {featur,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23701] {featur,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23702] {featur,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [23703] {featur,
## dataset,
## propos,
## work} => {task} 0.1000000 0.7500000 2.045455 3
## [23704] {task,
## dataset,
## propos,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [23705] {network,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23706] {network,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23707] {network,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [23708] {model,
## task,
## dataset,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [23709] {featur,
## task,
## dataset,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [23710] {featur,
## model,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23711] {featur,
## model,
## task,
## dataset} => {work} 0.1000000 1.0000000 2.500000 3
## [23712] {featur,
## model,
## dataset,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23713] {represent,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23714] {task,
## propos,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [23715] {represent,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23716] {represent,
## task,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [23717] {represent,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23718] {task,
## propos,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [23719] {model,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23720] {model,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23721] {model,
## task,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [23722] {model,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23723] {task,
## propos,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23724] {featur,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23725] {featur,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23726] {featur,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23727] {task,
## propos,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [23728] {network,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23729] {network,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23730] {network,
## task,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [23731] {network,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23732] {model,
## task,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [23733] {featur,
## task,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [23734] {featur,
## model,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [23735] {featur,
## model,
## work,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23736] {model,
## task,
## propos,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [23737] {featur,
## task,
## propos,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [23738] {featur,
## model,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [23739] {featur,
## model,
## task,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [23740] {featur,
## model,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [23741] {data,
## task,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [23742] {data,
## featur,
## task,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [23743] {featur,
## task,
## perform,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [23744] {data,
## featur,
## perform,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [23745] {data,
## task,
## dataset,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [23746] {data,
## represent,
## task,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [23747] {data,
## represent,
## task,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [23748] {represent,
## task,
## dataset,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [23749] {data,
## represent,
## dataset,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [23750] {data,
## show,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [23751] {show,
## task,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23752] {data,
## show,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23753] {data,
## task,
## dataset,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [23754] {data,
## task,
## dataset,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [23755] {data,
## task,
## propos,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [23756] {task,
## dataset,
## propos,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [23757] {data,
## dataset,
## propos,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [23758] {data,
## model,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [23759] {model,
## task,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23760] {data,
## model,
## dataset,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23761] {data,
## task,
## dataset,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [23762] {data,
## featur,
## task,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [23763] {featur,
## task,
## dataset,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [23764] {data,
## featur,
## dataset,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [23765] {data,
## task,
## dataset,
## learn} => {network} 0.1333333 0.8000000 1.263158 4
## [23766] {data,
## network,
## task,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [23767] {data,
## network,
## task,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [23768] {network,
## task,
## dataset,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [23769] {data,
## network,
## dataset,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [23770] {data,
## represent,
## task,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [23771] {data,
## show,
## task,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [23772] {represent,
## show,
## task,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [23773] {data,
## represent,
## show,
## dataset} => {task} 0.1000000 1.0000000 2.727273 3
## [23774] {data,
## represent,
## task,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [23775] {data,
## task,
## dataset,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [23776] {data,
## represent,
## task,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23777] {represent,
## task,
## dataset,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [23778] {data,
## represent,
## dataset,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [23779] {data,
## represent,
## task,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [23780] {data,
## featur,
## task,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [23781] {featur,
## represent,
## task,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [23782] {data,
## represent,
## task,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [23783] {data,
## network,
## task,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [23784] {data,
## network,
## represent,
## task} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23785] {network,
## represent,
## task,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [23786] {data,
## network,
## represent,
## dataset} => {task} 0.1000000 0.7500000 2.045455 3
## [23787] {data,
## show,
## task,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [23788] {data,
## network,
## task,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [23789] {data,
## network,
## show,
## task} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23790] {network,
## show,
## task,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [23791] {data,
## network,
## show,
## dataset} => {task} 0.1000000 1.0000000 2.727273 3
## [23792] {data,
## task,
## dataset,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [23793] {data,
## model,
## task,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [23794] {data,
## model,
## task,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23795] {model,
## task,
## dataset,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [23796] {data,
## model,
## dataset,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [23797] {data,
## task,
## dataset,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [23798] {data,
## featur,
## task,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [23799] {featur,
## task,
## dataset,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [23800] {data,
## featur,
## dataset,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [23801] {data,
## task,
## dataset,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [23802] {data,
## network,
## task,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [23803] {data,
## network,
## task,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23804] {network,
## task,
## dataset,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [23805] {data,
## network,
## dataset,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [23806] {data,
## model,
## task,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [23807] {data,
## featur,
## task,
## dataset} => {model} 0.1000000 0.7500000 1.406250 3
## [23808] {featur,
## model,
## task,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [23809] {data,
## featur,
## model,
## dataset} => {task} 0.1000000 0.7500000 2.045455 3
## [23810] {data,
## featur,
## task,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [23811] {data,
## network,
## task,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [23812] {data,
## featur,
## network,
## task} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23813] {featur,
## network,
## task,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [23814] {data,
## featur,
## network,
## dataset} => {task} 0.1000000 0.7500000 2.045455 3
## [23815] {data,
## represent,
## task,
## learn} => {show} 0.1333333 0.8000000 1.500000 4
## [23816] {data,
## show,
## task,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [23817] {data,
## represent,
## show,
## task} => {learn} 0.1333333 0.8000000 1.846154 4
## [23818] {represent,
## show,
## task,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [23819] {data,
## represent,
## show,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [23820] {data,
## represent,
## task,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [23821] {represent,
## task,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [23822] {data,
## represent,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23823] {data,
## model,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [23824] {model,
## represent,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23825] {data,
## model,
## represent,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23826] {data,
## represent,
## task,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [23827] {featur,
## represent,
## task,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [23828] {data,
## featur,
## represent,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [23829] {data,
## network,
## task,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [23830] {data,
## network,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [23831] {network,
## represent,
## task,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [23832] {data,
## network,
## represent,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23833] {data,
## show,
## task,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23834] {show,
## task,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23835] {data,
## show,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23836] {data,
## model,
## show,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [23837] {model,
## show,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23838] {data,
## model,
## show,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23839] {data,
## show,
## task,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [23840] {data,
## featur,
## show,
## task} => {learn} 0.1333333 0.8000000 1.846154 4
## [23841] {featur,
## show,
## task,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [23842] {data,
## featur,
## show,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [23843] {data,
## network,
## task,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [23844] {data,
## network,
## show,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [23845] {network,
## show,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [23846] {data,
## network,
## show,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23847] {data,
## task,
## propos,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [23848] {data,
## model,
## task,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [23849] {data,
## model,
## task,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [23850] {model,
## task,
## propos,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [23851] {data,
## model,
## propos,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [23852] {data,
## task,
## propos,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [23853] {data,
## featur,
## task,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [23854] {featur,
## task,
## propos,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [23855] {data,
## featur,
## propos,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [23856] {data,
## network,
## task,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [23857] {data,
## network,
## task,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23858] {network,
## task,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [23859] {data,
## network,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23860] {data,
## model,
## task,
## learn} => {featur} 0.1666667 1.0000000 1.875000 5
## [23861] {data,
## featur,
## task,
## learn} => {model} 0.1666667 0.8333333 1.562500 5
## [23862] {data,
## featur,
## model,
## task} => {learn} 0.1666667 0.8333333 1.923077 5
## [23863] {featur,
## model,
## task,
## learn} => {data} 0.1666667 1.0000000 2.307692 5
## [23864] {data,
## featur,
## model,
## learn} => {task} 0.1666667 0.8333333 2.272727 5
## [23865] {data,
## network,
## task,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23866] {data,
## featur,
## network,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [23867] {featur,
## network,
## task,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [23868] {data,
## featur,
## network,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23869] {data,
## model,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [23870] {data,
## model,
## show,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [23871] {model,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [23872] {data,
## model,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [23873] {data,
## represent,
## show,
## task} => {featur} 0.1333333 0.8000000 1.500000 4
## [23874] {data,
## featur,
## show,
## task} => {represent} 0.1333333 0.8000000 1.600000 4
## [23875] {featur,
## represent,
## show,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [23876] {data,
## featur,
## represent,
## show} => {task} 0.1333333 1.0000000 2.727273 4
## [23877] {data,
## represent,
## show,
## task} => {network} 0.1333333 0.8000000 1.263158 4
## [23878] {data,
## network,
## represent,
## task} => {show} 0.1333333 1.0000000 1.875000 4
## [23879] {data,
## network,
## show,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [23880] {network,
## represent,
## show,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [23881] {data,
## network,
## represent,
## show} => {task} 0.1333333 1.0000000 2.727273 4
## [23882] {data,
## represent,
## task,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [23883] {featur,
## represent,
## task,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [23884] {data,
## featur,
## represent,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [23885] {data,
## model,
## represent,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [23886] {featur,
## model,
## represent,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [23887] {data,
## featur,
## model,
## represent} => {task} 0.1333333 0.8000000 2.181818 4
## [23888] {data,
## network,
## represent,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [23889] {data,
## featur,
## network,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [23890] {featur,
## network,
## represent,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [23891] {data,
## featur,
## network,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [23892] {data,
## model,
## show,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [23893] {data,
## featur,
## show,
## task} => {model} 0.1333333 0.8000000 1.500000 4
## [23894] {featur,
## model,
## show,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [23895] {data,
## featur,
## model,
## show} => {task} 0.1333333 1.0000000 2.727273 4
## [23896] {data,
## network,
## show,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [23897] {data,
## featur,
## network,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [23898] {featur,
## network,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [23899] {data,
## featur,
## network,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [23900] {data,
## model,
## task,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [23901] {data,
## featur,
## task,
## propos} => {model} 0.1333333 0.8000000 1.500000 4
## [23902] {featur,
## model,
## task,
## propos} => {data} 0.1333333 1.0000000 2.307692 4
## [23903] {data,
## featur,
## model,
## propos} => {task} 0.1333333 0.8000000 2.181818 4
## [23904] {data,
## model,
## network,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [23905] {data,
## featur,
## network,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [23906] {featur,
## model,
## network,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [23907] {data,
## featur,
## model,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [23908] {represent,
## task,
## dataset,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [23909] {show,
## task,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23910] {represent,
## show,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [23911] {represent,
## show,
## task,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23912] {represent,
## show,
## dataset,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23913] {represent,
## task,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [23914] {task,
## dataset,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [23915] {represent,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23916] {represent,
## task,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23917] {represent,
## task,
## dataset,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23918] {featur,
## task,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [23919] {featur,
## represent,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [23920] {featur,
## represent,
## dataset,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23921] {represent,
## task,
## dataset,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [23922] {network,
## task,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [23923] {network,
## represent,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [23924] {network,
## represent,
## task,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23925] {network,
## represent,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23926] {show,
## task,
## dataset,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [23927] {network,
## task,
## dataset,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [23928] {network,
## show,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [23929] {network,
## show,
## task,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [23930] {network,
## show,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23931] {task,
## dataset,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [23932] {model,
## task,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [23933] {model,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23934] {model,
## task,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23935] {task,
## dataset,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23936] {featur,
## task,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [23937] {featur,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23938] {featur,
## dataset,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23939] {task,
## dataset,
## propos,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [23940] {network,
## task,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [23941] {network,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23942] {network,
## task,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23943] {network,
## dataset,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23944] {model,
## task,
## dataset,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [23945] {featur,
## task,
## dataset,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [23946] {featur,
## model,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [23947] {featur,
## task,
## dataset,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [23948] {network,
## task,
## dataset,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23949] {featur,
## network,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [23950] {featur,
## network,
## task,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23951] {featur,
## network,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23952] {represent,
## show,
## task,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [23953] {network,
## represent,
## task,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [23954] {network,
## show,
## task,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [23955] {network,
## represent,
## show,
## task} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23956] {network,
## represent,
## show,
## dataset} => {task} 0.1000000 1.0000000 2.727273 3
## [23957] {model,
## task,
## dataset,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [23958] {featur,
## task,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [23959] {featur,
## model,
## task,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [23960] {featur,
## model,
## task,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [23961] {represent,
## show,
## task,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23962] {featur,
## show,
## task,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [23963] {featur,
## represent,
## show,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [23964] {represent,
## show,
## task,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [23965] {network,
## represent,
## task,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [23966] {network,
## show,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [23967] {network,
## represent,
## show,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [23968] {network,
## represent,
## show,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23969] {represent,
## task,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23970] {featur,
## represent,
## task,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [23971] {represent,
## task,
## propos,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [23972] {network,
## represent,
## task,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [23973] {network,
## task,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [23974] {network,
## represent,
## task,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23975] {network,
## represent,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23976] {model,
## represent,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [23977] {featur,
## model,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [23978] {network,
## represent,
## task,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23979] {featur,
## network,
## task,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [23980] {featur,
## network,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [23981] {featur,
## network,
## represent,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [23982] {model,
## show,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [23983] {featur,
## show,
## task,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [23984] {featur,
## model,
## show,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [23985] {model,
## task,
## propos,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [23986] {featur,
## task,
## propos,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [23987] {featur,
## model,
## task,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [23988] {featur,
## model,
## task,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [23989] {featur,
## model,
## propos,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [23990] {network,
## task,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [23991] {featur,
## network,
## task,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [23992] {featur,
## network,
## task,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [23993] {featur,
## network,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [23994] {model,
## represent,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [23995] {featur,
## represent,
## show,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [23996] {featur,
## model,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [23997] {featur,
## model,
## show,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [23998] {featur,
## model,
## represent,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [23999] {featur,
## represent,
## show,
## task} => {network} 0.1000000 0.7500000 1.184211 3
## [24000] {network,
## represent,
## show,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [24001] {featur,
## network,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [24002] {featur,
## network,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [24003] {featur,
## network,
## represent,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [24004] {approach,
## train,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [24005] {approach,
## network,
## train,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [24006] {approach,
## network,
## train,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24007] {network,
## train,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [24008] {data,
## train,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24009] {data,
## represent,
## train,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [24010] {data,
## represent,
## train,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24011] {represent,
## train,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24012] {data,
## represent,
## train,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [24013] {data,
## featur,
## train,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [24014] {data,
## featur,
## represent,
## train} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24015] {featur,
## represent,
## train,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [24016] {data,
## represent,
## train,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [24017] {data,
## network,
## train,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [24018] {data,
## network,
## represent,
## train} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24019] {network,
## represent,
## train,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [24020] {data,
## network,
## represent,
## dataset} => {train} 0.1000000 0.7500000 1.875000 3
## [24021] {data,
## represent,
## show,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [24022] {data,
## network,
## represent,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [24023] {data,
## network,
## show,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [24024] {network,
## represent,
## show,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [24025] {data,
## network,
## represent,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [24026] {data,
## featur,
## represent,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [24027] {data,
## network,
## represent,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [24028] {data,
## featur,
## network,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [24029] {featur,
## network,
## represent,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [24030] {data,
## featur,
## network,
## represent} => {train} 0.1000000 0.7500000 1.875000 3
## [24031] {approach,
## data,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24032] {approach,
## data,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24033] {approach,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24034] {approach,
## data,
## dataset,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [24035] {data,
## dataset,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24036] {approach,
## data,
## dataset,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [24037] {approach,
## data,
## represent,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24038] {approach,
## represent,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [24039] {approach,
## data,
## represent,
## dataset} => {work} 0.1000000 1.0000000 2.500000 3
## [24040] {data,
## represent,
## dataset,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [24041] {approach,
## data,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [24042] {approach,
## data,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24043] {approach,
## dataset,
## propos,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [24044] {approach,
## data,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [24045] {data,
## dataset,
## propos,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [24046] {approach,
## data,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24047] {approach,
## data,
## represent,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24048] {approach,
## represent,
## work,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [24049] {approach,
## data,
## represent,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [24050] {data,
## represent,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24051] {approach,
## data,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24052] {approach,
## data,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24053] {approach,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24054] {approach,
## data,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [24055] {data,
## propos,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24056] {approach,
## data,
## represent,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [24057] {approach,
## data,
## propos,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [24058] {approach,
## represent,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [24059] {approach,
## data,
## represent,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [24060] {data,
## represent,
## propos,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [24061] {approach,
## dataset,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24062] {approach,
## represent,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24063] {approach,
## represent,
## work,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24064] {approach,
## represent,
## dataset,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24065] {represent,
## dataset,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24066] {approach,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24067] {approach,
## dataset,
## propos,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [24068] {approach,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24069] {approach,
## dataset,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24070] {dataset,
## propos,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24071] {approach,
## represent,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [24072] {approach,
## dataset,
## propos,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [24073] {approach,
## represent,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24074] {approach,
## represent,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [24075] {represent,
## dataset,
## propos,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [24076] {approach,
## show,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [24077] {approach,
## dataset,
## propos,
## work} => {show} 0.1000000 0.7500000 1.406250 3
## [24078] {approach,
## show,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24079] {show,
## dataset,
## propos,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [24080] {approach,
## show,
## dataset,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [24081] {approach,
## network,
## dataset,
## work} => {show} 0.1000000 1.0000000 1.875000 3
## [24082] {approach,
## network,
## show,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24083] {approach,
## network,
## show,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [24084] {network,
## show,
## dataset,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [24085] {approach,
## dataset,
## propos,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [24086] {approach,
## network,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [24087] {approach,
## network,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24088] {approach,
## represent,
## work,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [24089] {approach,
## show,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24090] {approach,
## represent,
## show,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24091] {approach,
## represent,
## show,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24092] {represent,
## show,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24093] {approach,
## represent,
## work,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24094] {approach,
## propos,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24095] {approach,
## represent,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24096] {represent,
## propos,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24097] {approach,
## represent,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24098] {approach,
## model,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24099] {approach,
## model,
## represent,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24100] {approach,
## model,
## represent,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24101] {model,
## represent,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24102] {approach,
## represent,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24103] {approach,
## featur,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24104] {approach,
## featur,
## represent,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24105] {approach,
## featur,
## represent,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24106] {featur,
## represent,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24107] {approach,
## represent,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [24108] {approach,
## network,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24109] {approach,
## network,
## represent,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24110] {approach,
## network,
## represent,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24111] {network,
## represent,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24112] {approach,
## show,
## work,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [24113] {approach,
## network,
## work,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [24114] {approach,
## network,
## show,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [24115] {approach,
## network,
## show,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [24116] {network,
## show,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24117] {approach,
## model,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [24118] {approach,
## featur,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24119] {approach,
## featur,
## model,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24120] {approach,
## featur,
## model,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24121] {featur,
## model,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24122] {approach,
## represent,
## show,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [24123] {approach,
## network,
## represent,
## work} => {show} 0.1000000 1.0000000 1.875000 3
## [24124] {approach,
## network,
## show,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [24125] {approach,
## network,
## represent,
## show} => {work} 0.1000000 0.7500000 1.875000 3
## [24126] {network,
## represent,
## show,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [24127] {approach,
## model,
## represent,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [24128] {approach,
## featur,
## represent,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [24129] {approach,
## featur,
## model,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [24130] {approach,
## featur,
## model,
## represent} => {work} 0.1000000 0.7500000 1.875000 3
## [24131] {featur,
## model,
## represent,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [24132] {approach,
## show,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [24133] {approach,
## network,
## show,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [24134] {approach,
## network,
## propos,
## work} => {show} 0.1000000 1.0000000 1.875000 3
## [24135] {approach,
## network,
## show,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [24136] {network,
## show,
## propos,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [24137] {approach,
## dataset,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24138] {approach,
## model,
## dataset,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [24139] {approach,
## model,
## perform,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24140] {approach,
## model,
## dataset,
## learn} => {perform} 0.1000000 0.7500000 1.607143 3
## [24141] {model,
## dataset,
## perform,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24142] {approach,
## show,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [24143] {approach,
## dataset,
## perform,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [24144] {approach,
## show,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24145] {show,
## dataset,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [24146] {approach,
## show,
## dataset,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [24147] {approach,
## model,
## dataset,
## perform} => {show} 0.1000000 0.7500000 1.406250 3
## [24148] {approach,
## model,
## show,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24149] {approach,
## model,
## show,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [24150] {model,
## show,
## dataset,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [24151] {approach,
## dataset,
## perform,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [24152] {approach,
## model,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [24153] {approach,
## model,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24154] {model,
## dataset,
## perform,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [24155] {approach,
## model,
## dataset,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [24156] {approach,
## featur,
## dataset,
## perform} => {model} 0.1000000 1.0000000 1.875000 3
## [24157] {approach,
## featur,
## model,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24158] {approach,
## featur,
## model,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [24159] {featur,
## model,
## dataset,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [24160] {approach,
## data,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24161] {approach,
## data,
## represent,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [24162] {approach,
## data,
## represent,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24163] {approach,
## represent,
## dataset,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [24164] {approach,
## data,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24165] {approach,
## data,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24166] {approach,
## data,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24167] {approach,
## dataset,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [24168] {approach,
## data,
## represent,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [24169] {approach,
## data,
## dataset,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [24170] {approach,
## data,
## represent,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24171] {approach,
## represent,
## dataset,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [24172] {data,
## represent,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [24173] {approach,
## data,
## represent,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24174] {approach,
## data,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24175] {approach,
## data,
## represent,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [24176] {data,
## represent,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24177] {approach,
## data,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [24178] {approach,
## data,
## network,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [24179] {approach,
## data,
## network,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [24180] {approach,
## network,
## represent,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [24181] {data,
## network,
## represent,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [24182] {approach,
## data,
## represent,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [24183] {approach,
## data,
## featur,
## represent} => {propos} 0.1000000 0.7500000 1.500000 3
## [24184] {approach,
## data,
## featur,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [24185] {approach,
## featur,
## represent,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [24186] {data,
## featur,
## represent,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [24187] {approach,
## data,
## model,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [24188] {approach,
## data,
## featur,
## represent} => {model} 0.1000000 0.7500000 1.406250 3
## [24189] {approach,
## data,
## featur,
## model} => {represent} 0.1000000 1.0000000 2.000000 3
## [24190] {approach,
## featur,
## model,
## represent} => {data} 0.1000000 0.7500000 1.730769 3
## [24191] {approach,
## represent,
## dataset,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [24192] {approach,
## show,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24193] {approach,
## represent,
## show,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [24194] {approach,
## represent,
## show,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24195] {represent,
## show,
## dataset,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24196] {approach,
## represent,
## dataset,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [24197] {approach,
## dataset,
## propos,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [24198] {approach,
## represent,
## dataset,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [24199] {approach,
## represent,
## propos,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [24200] {represent,
## dataset,
## propos,
## learn} => {approach} 0.1333333 0.8000000 2.000000 4
## [24201] {approach,
## represent,
## dataset,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24202] {approach,
## model,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24203] {approach,
## model,
## represent,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [24204] {approach,
## model,
## represent,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24205] {model,
## represent,
## dataset,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24206] {approach,
## show,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24207] {approach,
## dataset,
## propos,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [24208] {approach,
## show,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24209] {show,
## dataset,
## propos,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24210] {approach,
## show,
## dataset,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24211] {approach,
## model,
## dataset,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [24212] {approach,
## model,
## show,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [24213] {approach,
## model,
## show,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24214] {model,
## show,
## dataset,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24215] {approach,
## dataset,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24216] {approach,
## model,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24217] {approach,
## model,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24218] {approach,
## model,
## dataset,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24219] {approach,
## featur,
## dataset,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24220] {approach,
## featur,
## model,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [24221] {approach,
## featur,
## model,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24222] {approach,
## represent,
## show,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [24223] {approach,
## represent,
## dataset,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [24224] {approach,
## represent,
## show,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24225] {represent,
## show,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [24226] {approach,
## represent,
## dataset,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [24227] {approach,
## model,
## represent,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [24228] {approach,
## model,
## represent,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24229] {model,
## represent,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [24230] {approach,
## model,
## show,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [24231] {approach,
## model,
## show,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24232] {model,
## show,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [24233] {approach,
## show,
## dataset,
## propos} => {network} 0.1333333 0.8000000 1.263158 4
## [24234] {approach,
## network,
## show,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [24235] {approach,
## network,
## dataset,
## propos} => {show} 0.1333333 0.8000000 1.500000 4
## [24236] {approach,
## network,
## show,
## propos} => {dataset} 0.1333333 1.0000000 2.307692 4
## [24237] {network,
## show,
## dataset,
## propos} => {approach} 0.1333333 1.0000000 2.500000 4
## [24238] {approach,
## model,
## show,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [24239] {approach,
## featur,
## show,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [24240] {approach,
## featur,
## model,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [24241] {featur,
## model,
## show,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [24242] {approach,
## featur,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [24243] {approach,
## featur,
## model,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [24244] {approach,
## featur,
## model,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24245] {approach,
## model,
## network,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [24246] {approach,
## model,
## network,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24247] {model,
## network,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [24248] {approach,
## represent,
## show,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24249] {approach,
## show,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24250] {approach,
## represent,
## show,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24251] {represent,
## show,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24252] {approach,
## represent,
## show,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24253] {approach,
## model,
## represent,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [24254] {approach,
## model,
## show,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24255] {approach,
## model,
## represent,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [24256] {model,
## represent,
## show,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24257] {approach,
## represent,
## show,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [24258] {approach,
## network,
## represent,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [24259] {approach,
## network,
## show,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24260] {approach,
## network,
## represent,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [24261] {network,
## represent,
## show,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24262] {approach,
## model,
## represent,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24263] {approach,
## model,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24264] {approach,
## model,
## represent,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24265] {model,
## represent,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24266] {approach,
## featur,
## represent,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24267] {approach,
## featur,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24268] {approach,
## featur,
## represent,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [24269] {approach,
## network,
## represent,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24270] {approach,
## network,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24271] {approach,
## network,
## represent,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24272] {network,
## represent,
## propos,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24273] {approach,
## model,
## represent,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24274] {approach,
## featur,
## represent,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24275] {approach,
## featur,
## model,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24276] {approach,
## featur,
## model,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [24277] {approach,
## featur,
## represent,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [24278] {approach,
## network,
## represent,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24279] {approach,
## featur,
## network,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24280] {approach,
## featur,
## network,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [24281] {featur,
## network,
## represent,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [24282] {approach,
## model,
## show,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24283] {approach,
## featur,
## show,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24284] {approach,
## featur,
## model,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [24285] {approach,
## model,
## represent,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [24286] {approach,
## featur,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [24287] {approach,
## featur,
## model,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [24288] {featur,
## model,
## represent,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [24289] {approach,
## model,
## represent,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [24290] {approach,
## network,
## represent,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [24291] {approach,
## model,
## network,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [24292] {approach,
## model,
## network,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [24293] {model,
## network,
## represent,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [24294] {approach,
## featur,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [24295] {approach,
## network,
## represent,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [24296] {approach,
## featur,
## network,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [24297] {approach,
## featur,
## network,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [24298] {featur,
## network,
## represent,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [24299] {approach,
## featur,
## model,
## represent} => {network} 0.1000000 0.7500000 1.184211 3
## [24300] {approach,
## model,
## network,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [24301] {approach,
## featur,
## network,
## represent} => {model} 0.1000000 0.7500000 1.406250 3
## [24302] {approach,
## featur,
## model,
## network} => {represent} 0.1000000 0.7500000 1.500000 3
## [24303] {featur,
## model,
## network,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [24304] {approach,
## featur,
## model,
## show} => {network} 0.1333333 0.8000000 1.263158 4
## [24305] {approach,
## model,
## network,
## show} => {featur} 0.1333333 1.0000000 1.875000 4
## [24306] {approach,
## featur,
## network,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [24307] {approach,
## featur,
## model,
## network} => {show} 0.1333333 1.0000000 1.875000 4
## [24308] {featur,
## model,
## network,
## show} => {approach} 0.1333333 1.0000000 2.500000 4
## [24309] {data,
## dataset,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24310] {data,
## represent,
## dataset,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [24311] {data,
## represent,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24312] {represent,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24313] {data,
## dataset,
## work,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [24314] {data,
## dataset,
## propos,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [24315] {data,
## propos,
## work,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [24316] {dataset,
## propos,
## work,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [24317] {data,
## dataset,
## propos,
## learn} => {work} 0.1333333 0.8000000 2.000000 4
## [24318] {data,
## dataset,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24319] {data,
## model,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24320] {data,
## model,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24321] {model,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24322] {data,
## model,
## dataset,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24323] {data,
## dataset,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24324] {data,
## featur,
## dataset,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [24325] {data,
## featur,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24326] {featur,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24327] {data,
## dataset,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [24328] {data,
## network,
## dataset,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [24329] {data,
## network,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24330] {network,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24331] {data,
## network,
## dataset,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24332] {data,
## represent,
## dataset,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [24333] {data,
## dataset,
## propos,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [24334] {data,
## represent,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24335] {represent,
## dataset,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [24336] {data,
## represent,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [24337] {data,
## represent,
## dataset,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [24338] {data,
## featur,
## dataset,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [24339] {data,
## featur,
## represent,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24340] {featur,
## represent,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [24341] {data,
## represent,
## dataset,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [24342] {data,
## network,
## dataset,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [24343] {data,
## network,
## represent,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24344] {network,
## represent,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [24345] {data,
## network,
## represent,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [24346] {data,
## dataset,
## propos,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [24347] {data,
## model,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [24348] {data,
## model,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24349] {model,
## dataset,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [24350] {data,
## model,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [24351] {data,
## dataset,
## propos,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [24352] {data,
## featur,
## dataset,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [24353] {data,
## featur,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24354] {featur,
## dataset,
## propos,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [24355] {data,
## featur,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [24356] {data,
## dataset,
## propos,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [24357] {data,
## network,
## dataset,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [24358] {data,
## network,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24359] {data,
## network,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [24360] {data,
## model,
## dataset,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [24361] {data,
## featur,
## dataset,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [24362] {data,
## featur,
## model,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24363] {featur,
## model,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [24364] {data,
## featur,
## model,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [24365] {data,
## featur,
## dataset,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [24366] {data,
## network,
## dataset,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [24367] {data,
## featur,
## network,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24368] {featur,
## network,
## dataset,
## work} => {data} 0.1000000 0.7500000 1.730769 3
## [24369] {data,
## featur,
## network,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [24370] {data,
## represent,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24371] {data,
## propos,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24372] {data,
## represent,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24373] {represent,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24374] {data,
## represent,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24375] {data,
## propos,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24376] {data,
## model,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24377] {data,
## model,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24378] {model,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24379] {data,
## propos,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24380] {data,
## featur,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24381] {data,
## featur,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24382] {featur,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24383] {data,
## propos,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [24384] {data,
## network,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24385] {data,
## network,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24386] {network,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24387] {data,
## network,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [24388] {data,
## model,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [24389] {data,
## featur,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24390] {data,
## featur,
## model,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24391] {featur,
## model,
## work,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [24392] {data,
## model,
## propos,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [24393] {data,
## featur,
## propos,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [24394] {data,
## featur,
## model,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [24395] {featur,
## model,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [24396] {represent,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24397] {dataset,
## propos,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24398] {represent,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24399] {represent,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24400] {dataset,
## propos,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24401] {model,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24402] {model,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24403] {model,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24404] {dataset,
## propos,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24405] {featur,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24406] {featur,
## dataset,
## propos,
## work} => {learn} 0.1000000 0.7500000 1.730769 3
## [24407] {featur,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24408] {featur,
## dataset,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24409] {dataset,
## propos,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [24410] {network,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24411] {network,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24412] {network,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [24413] {model,
## dataset,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [24414] {featur,
## dataset,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24415] {featur,
## model,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24416] {featur,
## model,
## work,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24417] {show,
## dataset,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [24418] {network,
## show,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [24419] {network,
## show,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24420] {network,
## show,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [24421] {model,
## dataset,
## propos,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [24422] {featur,
## dataset,
## propos,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [24423] {featur,
## model,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [24424] {featur,
## model,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24425] {featur,
## dataset,
## propos,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [24426] {featur,
## network,
## dataset,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [24427] {featur,
## network,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24428] {featur,
## network,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [24429] {represent,
## show,
## work,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [24430] {network,
## represent,
## work,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [24431] {network,
## show,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24432] {network,
## represent,
## show,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24433] {network,
## represent,
## show,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [24434] {model,
## represent,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [24435] {featur,
## represent,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24436] {featur,
## model,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24437] {featur,
## model,
## represent,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24438] {model,
## propos,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [24439] {featur,
## propos,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24440] {featur,
## model,
## work,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24441] {featur,
## model,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24442] {featur,
## model,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [24443] {model,
## network,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [24444] {featur,
## network,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24445] {featur,
## model,
## network,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [24446] {featur,
## model,
## network,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [24447] {data,
## represent,
## dataset,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [24448] {data,
## featur,
## dataset,
## perform} => {represent} 0.1000000 1.0000000 2.000000 3
## [24449] {data,
## featur,
## represent,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24450] {featur,
## represent,
## dataset,
## perform} => {data} 0.1000000 1.0000000 2.307692 3
## [24451] {data,
## perform,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24452] {data,
## model,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24453] {data,
## model,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24454] {model,
## perform,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [24455] {data,
## perform,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [24456] {data,
## featur,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24457] {data,
## featur,
## perform,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [24458] {featur,
## perform,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [24459] {data,
## model,
## perform,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [24460] {data,
## featur,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24461] {data,
## featur,
## model,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [24462] {featur,
## model,
## perform,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [24463] {data,
## represent,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [24464] {data,
## featur,
## represent,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [24465] {data,
## featur,
## perform,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [24466] {featur,
## represent,
## perform,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [24467] {data,
## featur,
## represent,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [24468] {data,
## model,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [24469] {data,
## featur,
## perform,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [24470] {data,
## featur,
## model,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [24471] {featur,
## model,
## perform,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [24472] {represent,
## dataset,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24473] {dataset,
## perform,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24474] {represent,
## dataset,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24475] {represent,
## perform,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24476] {represent,
## dataset,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24477] {model,
## dataset,
## perform,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24478] {model,
## represent,
## dataset,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [24479] {model,
## represent,
## perform,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24480] {model,
## represent,
## dataset,
## learn} => {perform} 0.1000000 0.7500000 1.607143 3
## [24481] {dataset,
## perform,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24482] {model,
## dataset,
## perform,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24483] {model,
## dataset,
## perform,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [24484] {model,
## perform,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24485] {model,
## dataset,
## perform,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24486] {featur,
## dataset,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [24487] {featur,
## model,
## dataset,
## perform} => {learn} 0.1000000 0.7500000 1.730769 3
## [24488] {featur,
## model,
## perform,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24489] {represent,
## dataset,
## perform,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [24490] {model,
## represent,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [24491] {model,
## dataset,
## perform,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [24492] {model,
## represent,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24493] {model,
## represent,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [24494] {model,
## dataset,
## perform,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [24495] {featur,
## dataset,
## perform,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [24496] {featur,
## model,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [24497] {featur,
## model,
## perform,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24498] {represent,
## perform,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24499] {model,
## represent,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24500] {model,
## perform,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24501] {model,
## represent,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24502] {model,
## represent,
## propos,
## learn} => {perform} 0.1000000 0.7500000 1.607143 3
## [24503] {represent,
## perform,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24504] {featur,
## represent,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [24505] {featur,
## perform,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24506] {featur,
## represent,
## perform,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [24507] {model,
## perform,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24508] {featur,
## perform,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24509] {featur,
## model,
## perform,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24510] {featur,
## model,
## perform,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [24511] {data,
## show,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24512] {data,
## represent,
## show,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [24513] {data,
## represent,
## show,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24514] {represent,
## show,
## dataset,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [24515] {data,
## represent,
## dataset,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [24516] {data,
## dataset,
## propos,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [24517] {data,
## represent,
## dataset,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [24518] {data,
## represent,
## propos,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [24519] {represent,
## dataset,
## propos,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [24520] {data,
## model,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24521] {data,
## model,
## represent,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [24522] {data,
## model,
## represent,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24523] {model,
## represent,
## dataset,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [24524] {data,
## represent,
## dataset,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [24525] {data,
## featur,
## dataset,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [24526] {data,
## featur,
## represent,
## dataset} => {learn} 0.1333333 0.8000000 1.846154 4
## [24527] {data,
## featur,
## represent,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [24528] {featur,
## represent,
## dataset,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [24529] {data,
## network,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24530] {data,
## network,
## represent,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [24531] {data,
## network,
## represent,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24532] {network,
## represent,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24533] {data,
## show,
## dataset,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [24534] {data,
## network,
## dataset,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [24535] {data,
## network,
## show,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [24536] {data,
## network,
## show,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24537] {network,
## show,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24538] {data,
## dataset,
## propos,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [24539] {data,
## model,
## dataset,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [24540] {data,
## model,
## dataset,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [24541] {data,
## model,
## propos,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [24542] {model,
## dataset,
## propos,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [24543] {data,
## dataset,
## propos,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [24544] {data,
## featur,
## dataset,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [24545] {data,
## featur,
## dataset,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [24546] {data,
## featur,
## propos,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [24547] {featur,
## dataset,
## propos,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [24548] {data,
## network,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24549] {data,
## network,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24550] {data,
## network,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24551] {network,
## dataset,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24552] {data,
## model,
## dataset,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [24553] {data,
## featur,
## dataset,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [24554] {data,
## featur,
## model,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [24555] {featur,
## model,
## dataset,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [24556] {data,
## network,
## dataset,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24557] {data,
## featur,
## network,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [24558] {data,
## featur,
## network,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24559] {featur,
## network,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [24560] {data,
## represent,
## show,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [24561] {data,
## network,
## represent,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [24562] {data,
## network,
## show,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [24563] {data,
## network,
## represent,
## show} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24564] {network,
## represent,
## show,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [24565] {data,
## represent,
## dataset,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [24566] {data,
## model,
## represent,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [24567] {data,
## model,
## dataset,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [24568] {data,
## model,
## represent,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24569] {model,
## represent,
## dataset,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [24570] {data,
## represent,
## dataset,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [24571] {data,
## featur,
## dataset,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [24572] {data,
## featur,
## represent,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24573] {featur,
## represent,
## dataset,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [24574] {data,
## model,
## represent,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [24575] {data,
## featur,
## model,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [24576] {featur,
## model,
## represent,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [24577] {data,
## network,
## represent,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [24578] {data,
## featur,
## network,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [24579] {data,
## featur,
## network,
## represent} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24580] {featur,
## network,
## represent,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [24581] {data,
## model,
## dataset,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [24582] {data,
## featur,
## dataset,
## propos} => {model} 0.1333333 1.0000000 1.875000 4
## [24583] {data,
## featur,
## model,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [24584] {data,
## featur,
## model,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [24585] {featur,
## model,
## dataset,
## propos} => {data} 0.1333333 0.8000000 1.846154 4
## [24586] {data,
## represent,
## show,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24587] {data,
## featur,
## show,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24588] {data,
## featur,
## represent,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [24589] {data,
## represent,
## show,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [24590] {data,
## network,
## represent,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [24591] {data,
## network,
## show,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24592] {data,
## network,
## represent,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [24593] {network,
## represent,
## show,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [24594] {data,
## represent,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24595] {data,
## model,
## represent,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24596] {data,
## model,
## represent,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24597] {model,
## represent,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [24598] {data,
## represent,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24599] {data,
## featur,
## represent,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [24600] {data,
## model,
## represent,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [24601] {data,
## featur,
## represent,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [24602] {data,
## featur,
## model,
## represent} => {learn} 0.1333333 0.8000000 1.846154 4
## [24603] {featur,
## model,
## represent,
## learn} => {data} 0.1333333 0.8000000 1.846154 4
## [24604] {data,
## model,
## show,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [24605] {data,
## featur,
## show,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24606] {data,
## featur,
## model,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [24607] {data,
## model,
## propos,
## learn} => {featur} 0.1666667 1.0000000 1.875000 5
## [24608] {data,
## featur,
## propos,
## learn} => {model} 0.1666667 1.0000000 1.875000 5
## [24609] {data,
## featur,
## model,
## learn} => {propos} 0.1666667 0.8333333 1.666667 5
## [24610] {data,
## featur,
## model,
## propos} => {learn} 0.1666667 1.0000000 2.307692 5
## [24611] {featur,
## model,
## propos,
## learn} => {data} 0.1666667 1.0000000 2.307692 5
## [24612] {data,
## model,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [24613] {data,
## featur,
## represent,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [24614] {data,
## featur,
## model,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [24615] {featur,
## model,
## represent,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [24616] {data,
## featur,
## represent,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [24617] {data,
## network,
## represent,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [24618] {data,
## featur,
## network,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [24619] {data,
## featur,
## network,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [24620] {featur,
## network,
## represent,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [24621] {data,
## model,
## represent,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [24622] {data,
## featur,
## represent,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [24623] {featur,
## model,
## represent,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [24624] {represent,
## show,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24625] {show,
## dataset,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24626] {represent,
## show,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24627] {represent,
## show,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24628] {represent,
## show,
## dataset,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [24629] {network,
## represent,
## dataset,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [24630] {network,
## show,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24631] {network,
## represent,
## show,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [24632] {network,
## represent,
## show,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [24633] {represent,
## dataset,
## propos,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [24634] {model,
## represent,
## dataset,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [24635] {model,
## dataset,
## propos,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [24636] {model,
## represent,
## dataset,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [24637] {model,
## represent,
## propos,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [24638] {featur,
## represent,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [24639] {featur,
## dataset,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [24640] {featur,
## represent,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24641] {model,
## represent,
## dataset,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24642] {featur,
## represent,
## dataset,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [24643] {featur,
## model,
## represent,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [24644] {model,
## dataset,
## propos,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [24645] {featur,
## dataset,
## propos,
## learn} => {model} 0.1333333 1.0000000 1.875000 4
## [24646] {featur,
## model,
## dataset,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [24647] {featur,
## model,
## dataset,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [24648] {featur,
## model,
## propos,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [24649] {model,
## represent,
## dataset,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [24650] {featur,
## represent,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [24651] {featur,
## model,
## represent,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [24652] {featur,
## model,
## represent,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24653] {model,
## network,
## dataset,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [24654] {featur,
## network,
## dataset,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [24655] {featur,
## model,
## network,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [24656] {featur,
## model,
## network,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24657] {model,
## represent,
## show,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24658] {featur,
## model,
## represent,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [24659] {network,
## represent,
## show,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24660] {featur,
## network,
## represent,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [24661] {featur,
## network,
## show,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24662] {featur,
## network,
## represent,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [24663] {model,
## represent,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [24664] {featur,
## model,
## represent,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [24665] {featur,
## model,
## represent,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [24666] {model,
## network,
## represent,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [24667] {featur,
## network,
## represent,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [24668] {featur,
## model,
## network,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [24669] {featur,
## model,
## network,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [24670] {approach,
## dataset,
## larger,
## propos,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [24671] {approach,
## model,
## dataset,
## larger,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [24672] {approach,
## model,
## larger,
## propos,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24673] {model,
## dataset,
## larger,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [24674] {approach,
## model,
## dataset,
## larger,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [24675] {approach,
## model,
## dataset,
## propos,
## result} => {larger} 0.1000000 1.0000000 10.000000 3
## [24676] {train,
## recognit,
## challeng,
## face,
## ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [24677] {represent,
## recognit,
## challeng,
## face,
## ident} => {train} 0.1000000 1.0000000 2.500000 3
## [24678] {represent,
## train,
## challeng,
## face,
## ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [24679] {represent,
## train,
## recognit,
## face,
## ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [24680] {represent,
## train,
## recognit,
## challeng,
## ident} => {face} 0.1000000 1.0000000 7.500000 3
## [24681] {represent,
## train,
## recognit,
## challeng,
## face} => {ident} 0.1000000 1.0000000 10.000000 3
## [24682] {dataset,
## improv,
## perform,
## shallow,
## tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [24683] {featur,
## improv,
## perform,
## shallow,
## tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24684] {featur,
## dataset,
## improv,
## shallow,
## tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [24685] {featur,
## dataset,
## perform,
## shallow,
## tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [24686] {featur,
## dataset,
## improv,
## perform,
## tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [24687] {featur,
## dataset,
## improv,
## perform,
## shallow} => {tradit} 0.1000000 1.0000000 10.000000 3
## [24688] {advantag,
## approach,
## classif,
## featur,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [24689] {advantag,
## approach,
## classif,
## method,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [24690] {advantag,
## classif,
## featur,
## method,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [24691] {advantag,
## approach,
## classif,
## featur,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [24692] {advantag,
## approach,
## featur,
## method,
## network} => {classif} 0.1000000 1.0000000 3.750000 3
## [24693] {approach,
## classif,
## featur,
## method,
## network} => {advantag} 0.1000000 1.0000000 10.000000 3
## [24694] {data,
## dataset,
## propos,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [24695] {data,
## model,
## dataset,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [24696] {data,
## model,
## dataset,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [24697] {data,
## model,
## propos,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24698] {model,
## dataset,
## propos,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [24699] {data,
## model,
## dataset,
## propos,
## learn} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [24700] {data,
## dataset,
## propos,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [24701] {data,
## featur,
## dataset,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [24702] {data,
## featur,
## dataset,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [24703] {data,
## featur,
## propos,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24704] {featur,
## dataset,
## propos,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [24705] {data,
## featur,
## dataset,
## propos,
## learn} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [24706] {data,
## model,
## dataset,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [24707] {data,
## featur,
## dataset,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [24708] {data,
## featur,
## model,
## dataset,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [24709] {data,
## featur,
## model,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24710] {featur,
## model,
## dataset,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [24711] {data,
## featur,
## model,
## dataset,
## learn} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [24712] {data,
## model,
## dataset,
## propos,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [24713] {data,
## featur,
## dataset,
## propos,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [24714] {data,
## featur,
## model,
## dataset,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [24715] {data,
## featur,
## model,
## propos,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24716] {featur,
## model,
## dataset,
## propos,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [24717] {data,
## featur,
## model,
## dataset,
## propos} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [24718] {data,
## model,
## propos,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [24719] {data,
## featur,
## propos,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [24720] {data,
## featur,
## model,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [24721] {data,
## featur,
## model,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [24722] {featur,
## model,
## propos,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [24723] {model,
## dataset,
## propos,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [24724] {featur,
## dataset,
## propos,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [24725] {featur,
## model,
## dataset,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [24726] {featur,
## model,
## dataset,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [24727] {featur,
## model,
## propos,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24728] {featur,
## model,
## dataset,
## propos,
## learn} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [24729] {train,
## dataset,
## neural,
## provid,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [24730] {network,
## train,
## neural,
## provid,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24731] {network,
## train,
## dataset,
## neural,
## provid} => {work} 0.1000000 1.0000000 2.500000 3
## [24732] {network,
## dataset,
## neural,
## provid,
## work} => {train} 0.1000000 1.0000000 2.500000 3
## [24733] {network,
## train,
## dataset,
## provid,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [24734] {network,
## train,
## dataset,
## neural,
## work} => {provid} 0.1000000 1.0000000 10.000000 3
## [24735] {classif,
## model,
## object,
## propos,
## test} => {featur} 0.1000000 1.0000000 1.875000 3
## [24736] {classif,
## featur,
## object,
## propos,
## test} => {model} 0.1000000 1.0000000 1.875000 3
## [24737] {classif,
## featur,
## model,
## object,
## test} => {propos} 0.1000000 1.0000000 2.000000 3
## [24738] {featur,
## model,
## object,
## propos,
## test} => {classif} 0.1000000 1.0000000 3.750000 3
## [24739] {classif,
## featur,
## model,
## propos,
## test} => {object} 0.1000000 1.0000000 3.750000 3
## [24740] {classif,
## featur,
## model,
## object,
## propos} => {test} 0.1000000 1.0000000 10.000000 3
## [24741] {show,
## extract,
## general,
## recognit,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [24742] {featur,
## extract,
## general,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [24743] {featur,
## show,
## extract,
## general,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [24744] {featur,
## show,
## extract,
## general,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [24745] {featur,
## show,
## extract,
## recognit,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [24746] {featur,
## show,
## general,
## recognit,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [24747] {architectur,
## perform,
## convolut,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [24748] {network,
## architectur,
## convolut,
## effici,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [24749] {network,
## architectur,
## perform,
## convolut,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [24750] {network,
## perform,
## convolut,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [24751] {network,
## architectur,
## perform,
## convolut,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [24752] {network,
## architectur,
## perform,
## effici,
## work} => {convolut} 0.1000000 1.0000000 7.500000 3
## [24753] {data,
## task,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [24754] {data,
## task,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24755] {task,
## dataset,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [24756] {data,
## dataset,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [24757] {data,
## task,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24758] {data,
## task,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24759] {data,
## task,
## dataset,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [24760] {data,
## featur,
## task,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24761] {featur,
## task,
## dataset,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [24762] {data,
## featur,
## dataset,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [24763] {data,
## featur,
## task,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24764] {data,
## featur,
## task,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24765] {data,
## task,
## dataset,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [24766] {data,
## network,
## task,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24767] {network,
## task,
## dataset,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [24768] {data,
## network,
## dataset,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [24769] {data,
## network,
## task,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24770] {data,
## network,
## task,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24771] {data,
## task,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [24772] {data,
## featur,
## task,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [24773] {featur,
## task,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [24774] {data,
## featur,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [24775] {data,
## featur,
## task,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24776] {data,
## featur,
## task,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24777] {data,
## task,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [24778] {data,
## network,
## task,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [24779] {network,
## task,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [24780] {data,
## network,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [24781] {data,
## network,
## task,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24782] {data,
## network,
## task,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24783] {data,
## featur,
## task,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [24784] {data,
## network,
## task,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [24785] {featur,
## network,
## task,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [24786] {data,
## featur,
## network,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [24787] {data,
## featur,
## network,
## task,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24788] {data,
## featur,
## network,
## task,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24789] {task,
## dataset,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [24790] {featur,
## task,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [24791] {featur,
## task,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24792] {featur,
## dataset,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [24793] {featur,
## task,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24794] {featur,
## task,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24795] {task,
## dataset,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [24796] {network,
## task,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [24797] {network,
## task,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24798] {network,
## dataset,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [24799] {network,
## task,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24800] {network,
## task,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24801] {featur,
## task,
## dataset,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [24802] {network,
## task,
## dataset,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [24803] {featur,
## network,
## task,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24804] {featur,
## network,
## dataset,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [24805] {featur,
## network,
## task,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24806] {featur,
## network,
## task,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24807] {featur,
## task,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [24808] {network,
## task,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [24809] {featur,
## network,
## task,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [24810] {featur,
## network,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [24811] {featur,
## network,
## task,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24812] {featur,
## network,
## task,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24813] {data,
## dataset,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [24814] {data,
## featur,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [24815] {data,
## featur,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24816] {featur,
## dataset,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [24817] {data,
## featur,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24818] {data,
## featur,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24819] {data,
## dataset,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [24820] {data,
## network,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [24821] {data,
## network,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24822] {network,
## dataset,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [24823] {data,
## network,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24824] {data,
## network,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24825] {data,
## featur,
## dataset,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [24826] {data,
## network,
## dataset,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [24827] {data,
## featur,
## network,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24828] {featur,
## network,
## dataset,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [24829] {data,
## featur,
## network,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24830] {data,
## featur,
## network,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24831] {data,
## featur,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [24832] {data,
## network,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [24833] {data,
## featur,
## network,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [24834] {featur,
## network,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [24835] {data,
## featur,
## network,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24836] {data,
## featur,
## network,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24837] {featur,
## dataset,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [24838] {network,
## dataset,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [24839] {featur,
## network,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [24840] {featur,
## network,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24841] {featur,
## network,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [24842] {featur,
## network,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [24843] {data,
## task,
## dataset,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [24844] {data,
## featur,
## task,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [24845] {data,
## featur,
## task,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24846] {featur,
## task,
## dataset,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [24847] {data,
## featur,
## dataset,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [24848] {data,
## featur,
## task,
## dataset,
## learn} => {care} 0.1000000 0.7500000 7.500000 3
## [24849] {data,
## task,
## dataset,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [24850] {data,
## network,
## task,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [24851] {data,
## network,
## task,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24852] {network,
## task,
## dataset,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [24853] {data,
## network,
## dataset,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [24854] {data,
## network,
## task,
## dataset,
## learn} => {care} 0.1000000 0.7500000 7.500000 3
## [24855] {data,
## featur,
## task,
## dataset,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [24856] {data,
## network,
## task,
## dataset,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [24857] {data,
## featur,
## network,
## task,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24858] {featur,
## network,
## task,
## dataset,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [24859] {data,
## featur,
## network,
## dataset,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [24860] {data,
## featur,
## network,
## task,
## dataset} => {care} 0.1000000 1.0000000 10.000000 3
## [24861] {data,
## featur,
## task,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [24862] {data,
## network,
## task,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [24863] {data,
## featur,
## network,
## task,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [24864] {featur,
## network,
## task,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [24865] {data,
## featur,
## network,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [24866] {data,
## featur,
## network,
## task,
## learn} => {care} 0.1000000 1.0000000 10.000000 3
## [24867] {featur,
## task,
## dataset,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [24868] {network,
## task,
## dataset,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [24869] {featur,
## network,
## task,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [24870] {featur,
## network,
## task,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24871] {featur,
## network,
## dataset,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [24872] {featur,
## network,
## task,
## dataset,
## learn} => {care} 0.1000000 1.0000000 10.000000 3
## [24873] {data,
## featur,
## dataset,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [24874] {data,
## network,
## dataset,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [24875] {data,
## featur,
## network,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [24876] {data,
## featur,
## network,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [24877] {featur,
## network,
## dataset,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [24878] {data,
## featur,
## network,
## dataset,
## learn} => {care} 0.1000000 1.0000000 10.000000 3
## [24879] {approach,
## featur,
## represent,
## captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [24880] {approach,
## network,
## represent,
## captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [24881] {approach,
## featur,
## network,
## captur,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [24882] {approach,
## featur,
## network,
## represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [24883] {featur,
## network,
## represent,
## captur,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [24884] {approach,
## featur,
## network,
## represent,
## learn} => {captur} 0.1000000 1.0000000 10.000000 3
## [24885] {boltzmann,
## machin,
## restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [24886] {boltzmann,
## machin,
## restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [24887] {boltzmann,
## restrict,
## show,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [24888] {boltzmann,
## machin,
## restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [24889] {boltzmann,
## machin,
## show,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24890] {machin,
## restrict,
## show,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24891] {boltzmann,
## machin,
## restrict,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [24892] {boltzmann,
## machin,
## model,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [24893] {boltzmann,
## model,
## restrict,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [24894] {boltzmann,
## machin,
## model,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [24895] {boltzmann,
## machin,
## model,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24896] {machin,
## model,
## restrict,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24897] {boltzmann,
## machin,
## restrict,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [24898] {boltzmann,
## machin,
## model,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [24899] {boltzmann,
## model,
## restrict,
## show,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [24900] {boltzmann,
## machin,
## model,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [24901] {boltzmann,
## machin,
## model,
## show,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24902] {machin,
## model,
## restrict,
## show,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24903] {boltzmann,
## restrict,
## show,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [24904] {boltzmann,
## model,
## restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [24905] {boltzmann,
## model,
## restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [24906] {boltzmann,
## model,
## restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [24907] {boltzmann,
## model,
## show,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24908] {model,
## restrict,
## show,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24909] {boltzmann,
## machin,
## restrict,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [24910] {boltzmann,
## machin,
## model,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [24911] {boltzmann,
## machin,
## model,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [24912] {boltzmann,
## model,
## restrict,
## show,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [24913] {boltzmann,
## machin,
## model,
## show,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24914] {machin,
## model,
## restrict,
## show,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24915] {boltzmann,
## data,
## machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [24916] {boltzmann,
## machin,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [24917] {boltzmann,
## data,
## machin,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [24918] {boltzmann,
## data,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [24919] {boltzmann,
## data,
## machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24920] {data,
## machin,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24921] {boltzmann,
## data,
## machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [24922] {boltzmann,
## machin,
## model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [24923] {boltzmann,
## data,
## machin,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [24924] {boltzmann,
## data,
## model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [24925] {boltzmann,
## data,
## machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24926] {data,
## machin,
## model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24927] {boltzmann,
## data,
## machin,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [24928] {boltzmann,
## featur,
## machin,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [24929] {boltzmann,
## data,
## featur,
## machin,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [24930] {boltzmann,
## data,
## featur,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [24931] {boltzmann,
## data,
## featur,
## machin,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24932] {data,
## featur,
## machin,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24933] {boltzmann,
## machin,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [24934] {boltzmann,
## machin,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [24935] {boltzmann,
## machin,
## model,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [24936] {boltzmann,
## model,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [24937] {boltzmann,
## machin,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24938] {machin,
## model,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24939] {boltzmann,
## machin,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [24940] {boltzmann,
## featur,
## machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [24941] {boltzmann,
## featur,
## machin,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [24942] {boltzmann,
## featur,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [24943] {boltzmann,
## featur,
## machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24944] {featur,
## machin,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24945] {boltzmann,
## machin,
## model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [24946] {boltzmann,
## featur,
## machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [24947] {boltzmann,
## featur,
## machin,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [24948] {boltzmann,
## featur,
## model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [24949] {boltzmann,
## featur,
## machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24950] {featur,
## machin,
## model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24951] {boltzmann,
## machin,
## restrict,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [24952] {boltzmann,
## machin,
## model,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [24953] {boltzmann,
## machin,
## model,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [24954] {boltzmann,
## model,
## restrict,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [24955] {boltzmann,
## machin,
## model,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24956] {machin,
## model,
## restrict,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24957] {boltzmann,
## data,
## machin,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [24958] {boltzmann,
## data,
## machin,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [24959] {boltzmann,
## machin,
## model,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [24960] {boltzmann,
## data,
## model,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [24961] {boltzmann,
## data,
## machin,
## model,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24962] {data,
## machin,
## model,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24963] {boltzmann,
## data,
## machin,
## restrict,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [24964] {boltzmann,
## data,
## featur,
## machin,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [24965] {boltzmann,
## featur,
## machin,
## restrict,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [24966] {boltzmann,
## data,
## featur,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [24967] {boltzmann,
## data,
## featur,
## machin,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24968] {data,
## featur,
## machin,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24969] {boltzmann,
## data,
## machin,
## model,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [24970] {boltzmann,
## data,
## featur,
## machin,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [24971] {boltzmann,
## featur,
## machin,
## model,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [24972] {boltzmann,
## data,
## featur,
## model,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [24973] {boltzmann,
## data,
## featur,
## machin,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24974] {data,
## featur,
## machin,
## model,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24975] {boltzmann,
## machin,
## model,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [24976] {boltzmann,
## featur,
## machin,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [24977] {boltzmann,
## featur,
## machin,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [24978] {boltzmann,
## featur,
## model,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [24979] {boltzmann,
## featur,
## machin,
## model,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24980] {featur,
## machin,
## model,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24981] {boltzmann,
## data,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [24982] {boltzmann,
## data,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [24983] {boltzmann,
## model,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [24984] {boltzmann,
## data,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [24985] {boltzmann,
## data,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24986] {data,
## model,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24987] {boltzmann,
## data,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [24988] {boltzmann,
## data,
## featur,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [24989] {boltzmann,
## featur,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [24990] {boltzmann,
## data,
## featur,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [24991] {boltzmann,
## data,
## featur,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24992] {data,
## featur,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24993] {boltzmann,
## data,
## model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [24994] {boltzmann,
## data,
## featur,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [24995] {boltzmann,
## featur,
## model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [24996] {boltzmann,
## data,
## featur,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [24997] {boltzmann,
## data,
## featur,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [24998] {data,
## featur,
## model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [24999] {boltzmann,
## model,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25000] {boltzmann,
## featur,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25001] {boltzmann,
## featur,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [25002] {boltzmann,
## featur,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25003] {boltzmann,
## featur,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [25004] {featur,
## model,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [25005] {boltzmann,
## data,
## model,
## restrict,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [25006] {boltzmann,
## data,
## featur,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [25007] {boltzmann,
## data,
## featur,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [25008] {boltzmann,
## featur,
## model,
## restrict,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [25009] {boltzmann,
## data,
## featur,
## model,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [25010] {data,
## featur,
## model,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [25011] {boltzmann,
## machin,
## show,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [25012] {boltzmann,
## machin,
## model,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [25013] {boltzmann,
## machin,
## model,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [25014] {boltzmann,
## model,
## show,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [25015] {boltzmann,
## machin,
## model,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [25016] {machin,
## model,
## show,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [25017] {boltzmann,
## data,
## machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25018] {boltzmann,
## data,
## machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [25019] {boltzmann,
## machin,
## model,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25020] {boltzmann,
## data,
## machin,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25021] {boltzmann,
## data,
## model,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [25022] {data,
## machin,
## model,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [25023] {boltzmann,
## data,
## machin,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25024] {boltzmann,
## data,
## featur,
## machin,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [25025] {boltzmann,
## featur,
## machin,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25026] {boltzmann,
## data,
## featur,
## machin,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25027] {boltzmann,
## data,
## featur,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [25028] {data,
## featur,
## machin,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [25029] {boltzmann,
## data,
## machin,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25030] {boltzmann,
## data,
## featur,
## machin,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25031] {boltzmann,
## featur,
## machin,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25032] {boltzmann,
## data,
## featur,
## machin,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [25033] {boltzmann,
## data,
## featur,
## model,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [25034] {data,
## featur,
## machin,
## model,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [25035] {boltzmann,
## machin,
## model,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25036] {boltzmann,
## featur,
## machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25037] {boltzmann,
## featur,
## machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [25038] {boltzmann,
## featur,
## machin,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25039] {boltzmann,
## featur,
## model,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [25040] {featur,
## machin,
## model,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [25041] {boltzmann,
## data,
## machin,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [25042] {boltzmann,
## data,
## featur,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [25043] {boltzmann,
## data,
## featur,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [25044] {boltzmann,
## featur,
## machin,
## model,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [25045] {boltzmann,
## data,
## featur,
## model,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [25046] {data,
## featur,
## machin,
## model,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [25047] {boltzmann,
## data,
## model,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25048] {boltzmann,
## data,
## featur,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25049] {boltzmann,
## data,
## featur,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [25050] {boltzmann,
## featur,
## model,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25051] {boltzmann,
## data,
## featur,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25052] {data,
## featur,
## model,
## show,
## task} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [25053] {machin,
## restrict,
## show,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [25054] {machin,
## model,
## restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [25055] {machin,
## model,
## restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [25056] {model,
## restrict,
## show,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [25057] {machin,
## model,
## restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [25058] {machin,
## model,
## show,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [25059] {data,
## machin,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25060] {data,
## machin,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [25061] {machin,
## model,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25062] {data,
## machin,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25063] {data,
## model,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [25064] {data,
## machin,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [25065] {data,
## machin,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25066] {data,
## featur,
## machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [25067] {featur,
## machin,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25068] {data,
## featur,
## machin,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25069] {data,
## featur,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [25070] {data,
## featur,
## machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [25071] {data,
## machin,
## model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25072] {data,
## featur,
## machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25073] {featur,
## machin,
## model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25074] {data,
## featur,
## machin,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [25075] {data,
## featur,
## model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [25076] {data,
## featur,
## machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [25077] {machin,
## model,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25078] {featur,
## machin,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25079] {featur,
## machin,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [25080] {featur,
## machin,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25081] {featur,
## model,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [25082] {featur,
## machin,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [25083] {data,
## machin,
## model,
## restrict,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [25084] {data,
## featur,
## machin,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [25085] {data,
## featur,
## machin,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [25086] {featur,
## machin,
## model,
## restrict,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [25087] {data,
## featur,
## model,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [25088] {data,
## featur,
## machin,
## model,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [25089] {data,
## model,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25090] {data,
## featur,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25091] {data,
## featur,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [25092] {featur,
## model,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25093] {data,
## featur,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25094] {data,
## featur,
## model,
## show,
## task} => {restrict} 0.1000000 0.7500000 5.625000 3
## [25095] {approach,
## model,
## algorithm,
## neural,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [25096] {approach,
## network,
## algorithm,
## neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [25097] {model,
## network,
## algorithm,
## neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [25098] {approach,
## model,
## network,
## neural,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [25099] {approach,
## model,
## network,
## algorithm,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [25100] {approach,
## model,
## network,
## algorithm,
## neural} => {order} 0.1000000 1.0000000 7.500000 3
## [25101] {model,
## dataset,
## demonstr,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [25102] {featur,
## dataset,
## demonstr,
## learn,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [25103] {featur,
## model,
## dataset,
## demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [25104] {featur,
## model,
## demonstr,
## learn,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25105] {featur,
## model,
## dataset,
## learn,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [25106] {featur,
## model,
## dataset,
## demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [25107] {data,
## model,
## paper,
## predict,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [25108] {data,
## featur,
## paper,
## predict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [25109] {featur,
## model,
## paper,
## predict,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [25110] {data,
## featur,
## model,
## paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [25111] {data,
## featur,
## model,
## predict,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [25112] {data,
## featur,
## model,
## paper,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [25113] {data,
## dataset,
## recognit,
## learn,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [25114] {data,
## featur,
## dataset,
## recognit,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [25115] {data,
## featur,
## recognit,
## learn,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25116] {featur,
## dataset,
## recognit,
## learn,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [25117] {data,
## featur,
## dataset,
## learn,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [25118] {data,
## featur,
## dataset,
## recognit,
## learn} => {face} 0.1000000 1.0000000 7.500000 3
## [25119] {approach,
## method,
## dataset,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [25120] {approach,
## method,
## show,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [25121] {approach,
## method,
## show,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25122] {method,
## show,
## dataset,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [25123] {approach,
## show,
## dataset,
## learn,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [25124] {approach,
## method,
## show,
## dataset,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [25125] {approach,
## method,
## dataset,
## learn,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [25126] {approach,
## method,
## model,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [25127] {approach,
## method,
## model,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25128] {method,
## model,
## dataset,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [25129] {approach,
## model,
## dataset,
## learn,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [25130] {approach,
## method,
## model,
## dataset,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [25131] {approach,
## method,
## show,
## dataset,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [25132] {approach,
## method,
## model,
## dataset,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [25133] {approach,
## method,
## model,
## show,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25134] {method,
## model,
## show,
## dataset,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [25135] {approach,
## model,
## show,
## dataset,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [25136] {approach,
## method,
## model,
## show,
## dataset} => {sourc} 0.1000000 0.7500000 5.625000 3
## [25137] {approach,
## method,
## show,
## learn,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [25138] {approach,
## method,
## model,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [25139] {approach,
## method,
## model,
## show,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [25140] {method,
## model,
## show,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [25141] {approach,
## model,
## show,
## learn,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [25142] {approach,
## method,
## model,
## show,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [25143] {method,
## show,
## dataset,
## learn,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [25144] {method,
## model,
## dataset,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [25145] {method,
## model,
## show,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [25146] {method,
## model,
## show,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25147] {model,
## show,
## dataset,
## learn,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [25148] {method,
## model,
## show,
## dataset,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [25149] {approach,
## represent,
## dataset,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [25150] {approach,
## show,
## dataset,
## learn,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [25151] {approach,
## represent,
## show,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [25152] {approach,
## represent,
## show,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25153] {represent,
## show,
## dataset,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [25154] {approach,
## represent,
## show,
## dataset,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [25155] {approach,
## represent,
## dataset,
## learn,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [25156] {approach,
## dataset,
## propos,
## learn,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [25157] {approach,
## represent,
## dataset,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [25158] {approach,
## represent,
## propos,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25159] {represent,
## dataset,
## propos,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [25160] {approach,
## represent,
## dataset,
## propos,
## learn} => {sourc} 0.1000000 0.7500000 5.625000 3
## [25161] {approach,
## show,
## dataset,
## learn,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [25162] {approach,
## dataset,
## propos,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [25163] {approach,
## show,
## dataset,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [25164] {approach,
## show,
## propos,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25165] {show,
## dataset,
## propos,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [25166] {approach,
## show,
## dataset,
## propos,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [25167] {approach,
## show,
## dataset,
## learn,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [25168] {approach,
## model,
## dataset,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [25169] {approach,
## model,
## show,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [25170] {approach,
## model,
## show,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25171] {model,
## show,
## dataset,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [25172] {approach,
## model,
## show,
## dataset,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [25173] {approach,
## represent,
## show,
## dataset,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [25174] {approach,
## represent,
## dataset,
## propos,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [25175] {approach,
## show,
## dataset,
## propos,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [25176] {approach,
## represent,
## show,
## propos,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25177] {represent,
## show,
## dataset,
## propos,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [25178] {approach,
## represent,
## show,
## dataset,
## propos} => {sourc} 0.1000000 1.0000000 7.500000 3
## [25179] {approach,
## represent,
## show,
## learn,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [25180] {approach,
## represent,
## propos,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [25181] {approach,
## show,
## propos,
## learn,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [25182] {approach,
## represent,
## show,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [25183] {represent,
## show,
## propos,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [25184] {approach,
## represent,
## show,
## propos,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [25185] {represent,
## show,
## dataset,
## learn,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [25186] {represent,
## dataset,
## propos,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [25187] {show,
## dataset,
## propos,
## learn,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [25188] {represent,
## show,
## dataset,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [25189] {represent,
## show,
## propos,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25190] {represent,
## show,
## dataset,
## propos,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [25191] {data,
## task,
## recognit,
## learn,
## variat} => {featur} 0.1000000 1.0000000 1.875000 3
## [25192] {data,
## featur,
## task,
## recognit,
## variat} => {learn} 0.1000000 1.0000000 2.307692 3
## [25193] {featur,
## task,
## recognit,
## learn,
## variat} => {data} 0.1000000 1.0000000 2.307692 3
## [25194] {data,
## featur,
## recognit,
## learn,
## variat} => {task} 0.1000000 1.0000000 2.727273 3
## [25195] {data,
## featur,
## task,
## learn,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [25196] {data,
## featur,
## task,
## recognit,
## learn} => {variat} 0.1000000 1.0000000 7.500000 3
## [25197] {approach,
## data,
## import,
## represent,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25198] {approach,
## data,
## import,
## model,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [25199] {approach,
## import,
## model,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25200] {data,
## import,
## model,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [25201] {approach,
## data,
## import,
## model,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [25202] {approach,
## data,
## model,
## represent,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [25203] {approach,
## data,
## import,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25204] {approach,
## data,
## featur,
## import,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [25205] {approach,
## featur,
## import,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25206] {data,
## featur,
## import,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [25207] {approach,
## data,
## featur,
## import,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [25208] {approach,
## data,
## featur,
## represent,
## task} => {import} 0.1000000 0.7500000 5.625000 3
## [25209] {approach,
## data,
## import,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25210] {approach,
## data,
## featur,
## import,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25211] {approach,
## featur,
## import,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25212] {data,
## featur,
## import,
## model,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [25213] {approach,
## data,
## featur,
## import,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [25214] {approach,
## data,
## featur,
## model,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [25215] {approach,
## import,
## model,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25216] {approach,
## featur,
## import,
## represent,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25217] {approach,
## featur,
## import,
## model,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [25218] {featur,
## import,
## model,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [25219] {approach,
## featur,
## import,
## model,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [25220] {approach,
## featur,
## model,
## represent,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [25221] {data,
## import,
## represent,
## task,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [25222] {data,
## import,
## model,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [25223] {data,
## import,
## model,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [25224] {import,
## model,
## represent,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [25225] {data,
## import,
## model,
## represent,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [25226] {data,
## model,
## represent,
## task,
## learn} => {import} 0.1000000 1.0000000 7.500000 3
## [25227] {data,
## import,
## represent,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [25228] {data,
## featur,
## import,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [25229] {data,
## featur,
## import,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [25230] {featur,
## import,
## represent,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [25231] {data,
## featur,
## import,
## represent,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [25232] {data,
## featur,
## represent,
## task,
## learn} => {import} 0.1000000 0.7500000 5.625000 3
## [25233] {data,
## import,
## model,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [25234] {data,
## featur,
## import,
## task,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [25235] {data,
## featur,
## import,
## model,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [25236] {featur,
## import,
## model,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [25237] {data,
## featur,
## import,
## model,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [25238] {data,
## import,
## represent,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25239] {data,
## import,
## model,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [25240] {data,
## import,
## model,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [25241] {import,
## model,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25242] {data,
## import,
## model,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25243] {data,
## model,
## represent,
## show,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [25244] {data,
## import,
## represent,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25245] {data,
## featur,
## import,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [25246] {data,
## featur,
## import,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [25247] {featur,
## import,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25248] {data,
## featur,
## import,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25249] {data,
## featur,
## represent,
## show,
## task} => {import} 0.1000000 0.7500000 5.625000 3
## [25250] {data,
## import,
## model,
## represent,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [25251] {data,
## featur,
## import,
## represent,
## task} => {model} 0.1333333 1.0000000 1.875000 4
## [25252] {data,
## featur,
## import,
## model,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [25253] {featur,
## import,
## model,
## represent,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [25254] {data,
## featur,
## import,
## model,
## represent} => {task} 0.1333333 1.0000000 2.727273 4
## [25255] {data,
## featur,
## model,
## represent,
## task} => {import} 0.1333333 1.0000000 7.500000 4
## [25256] {data,
## import,
## model,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25257] {data,
## featur,
## import,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25258] {data,
## featur,
## import,
## model,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [25259] {featur,
## import,
## model,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25260] {data,
## featur,
## import,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25261] {data,
## featur,
## model,
## show,
## task} => {import} 0.1000000 0.7500000 5.625000 3
## [25262] {import,
## model,
## represent,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [25263] {featur,
## import,
## represent,
## task,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [25264] {featur,
## import,
## model,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [25265] {featur,
## import,
## model,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [25266] {featur,
## import,
## model,
## represent,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [25267] {featur,
## model,
## represent,
## task,
## learn} => {import} 0.1000000 1.0000000 7.500000 3
## [25268] {import,
## model,
## represent,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25269] {featur,
## import,
## represent,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25270] {featur,
## import,
## model,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [25271] {featur,
## import,
## model,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [25272] {featur,
## import,
## model,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25273] {featur,
## model,
## represent,
## show,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [25274] {approach,
## data,
## import,
## model,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [25275] {approach,
## data,
## featur,
## import,
## represent} => {model} 0.1000000 1.0000000 1.875000 3
## [25276] {approach,
## data,
## featur,
## import,
## model} => {represent} 0.1000000 1.0000000 2.000000 3
## [25277] {approach,
## featur,
## import,
## model,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [25278] {data,
## featur,
## import,
## model,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [25279] {approach,
## data,
## featur,
## model,
## represent} => {import} 0.1000000 1.0000000 7.500000 3
## [25280] {data,
## import,
## model,
## represent,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [25281] {data,
## featur,
## import,
## represent,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [25282] {data,
## featur,
## import,
## model,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [25283] {data,
## featur,
## import,
## model,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [25284] {featur,
## import,
## model,
## represent,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [25285] {data,
## featur,
## model,
## represent,
## learn} => {import} 0.1000000 0.7500000 5.625000 3
## [25286] {data,
## import,
## model,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [25287] {data,
## featur,
## import,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [25288] {data,
## featur,
## import,
## model,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [25289] {data,
## featur,
## import,
## model,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [25290] {featur,
## import,
## model,
## represent,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [25291] {data,
## featur,
## model,
## represent,
## show} => {import} 0.1000000 1.0000000 7.500000 3
## [25292] {perform,
## problem,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25293] {perform,
## propos,
## problem,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25294] {propos,
## problem,
## learn,
## art,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [25295] {perform,
## propos,
## learn,
## art,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [25296] {perform,
## propos,
## problem,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25297] {perform,
## propos,
## problem,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25298] {perform,
## problem,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25299] {model,
## perform,
## problem,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25300] {model,
## problem,
## learn,
## art,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [25301] {model,
## perform,
## learn,
## art,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [25302] {model,
## perform,
## problem,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25303] {model,
## perform,
## problem,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25304] {perform,
## propos,
## problem,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25305] {model,
## perform,
## problem,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25306] {model,
## propos,
## problem,
## art,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [25307] {model,
## perform,
## propos,
## art,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [25308] {model,
## perform,
## propos,
## problem,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25309] {model,
## perform,
## propos,
## problem,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25310] {propos,
## problem,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25311] {model,
## problem,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25312] {model,
## propos,
## problem,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25313] {model,
## propos,
## learn,
## art,
## state} => {problem} 0.1000000 0.7500000 2.500000 3
## [25314] {model,
## propos,
## problem,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25315] {model,
## propos,
## problem,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25316] {data,
## task,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25317] {data,
## task,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25318] {task,
## propos,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25319] {data,
## propos,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25320] {data,
## task,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25321] {data,
## task,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25322] {data,
## task,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25323] {data,
## model,
## task,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25324] {model,
## task,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25325] {data,
## model,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25326] {data,
## model,
## task,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25327] {data,
## model,
## task,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25328] {data,
## task,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25329] {data,
## featur,
## task,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25330] {featur,
## task,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25331] {data,
## featur,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25332] {data,
## featur,
## task,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25333] {data,
## featur,
## task,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25334] {data,
## task,
## propos,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25335] {data,
## model,
## task,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25336] {model,
## task,
## propos,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25337] {data,
## model,
## propos,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25338] {data,
## model,
## task,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25339] {data,
## model,
## task,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25340] {data,
## task,
## propos,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25341] {data,
## featur,
## task,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25342] {featur,
## task,
## propos,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25343] {data,
## featur,
## propos,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25344] {data,
## featur,
## task,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25345] {data,
## featur,
## task,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25346] {data,
## model,
## task,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25347] {data,
## featur,
## task,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25348] {featur,
## model,
## task,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25349] {data,
## featur,
## model,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25350] {data,
## featur,
## model,
## task,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25351] {data,
## featur,
## model,
## task,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25352] {task,
## propos,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25353] {model,
## task,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25354] {model,
## task,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25355] {model,
## propos,
## learn,
## art,
## state} => {task} 0.1000000 0.7500000 2.045455 3
## [25356] {model,
## task,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25357] {model,
## task,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25358] {task,
## propos,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25359] {featur,
## task,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25360] {featur,
## task,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25361] {featur,
## propos,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25362] {featur,
## task,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25363] {featur,
## task,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25364] {model,
## task,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25365] {featur,
## task,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25366] {featur,
## model,
## task,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25367] {featur,
## model,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25368] {featur,
## model,
## task,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25369] {featur,
## model,
## task,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25370] {model,
## task,
## propos,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25371] {featur,
## task,
## propos,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25372] {featur,
## model,
## task,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25373] {featur,
## model,
## propos,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25374] {featur,
## model,
## task,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25375] {featur,
## model,
## task,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25376] {perform,
## propos,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25377] {model,
## perform,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25378] {model,
## perform,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25379] {model,
## propos,
## learn,
## art,
## state} => {perform} 0.1000000 0.7500000 1.607143 3
## [25380] {model,
## perform,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25381] {model,
## perform,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25382] {data,
## propos,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25383] {data,
## model,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25384] {data,
## model,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25385] {model,
## propos,
## learn,
## art,
## state} => {data} 0.1000000 0.7500000 1.730769 3
## [25386] {data,
## model,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25387] {data,
## model,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25388] {data,
## propos,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25389] {data,
## featur,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25390] {data,
## featur,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25391] {featur,
## propos,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25392] {data,
## featur,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25393] {data,
## featur,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25394] {data,
## model,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25395] {data,
## featur,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25396] {data,
## featur,
## model,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25397] {featur,
## model,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25398] {data,
## featur,
## model,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25399] {data,
## featur,
## model,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25400] {data,
## model,
## propos,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25401] {data,
## featur,
## propos,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25402] {data,
## featur,
## model,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25403] {featur,
## model,
## propos,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25404] {data,
## featur,
## model,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25405] {data,
## featur,
## model,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25406] {dataset,
## propos,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25407] {model,
## dataset,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25408] {model,
## dataset,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25409] {model,
## propos,
## learn,
## art,
## state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [25410] {model,
## dataset,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25411] {model,
## dataset,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25412] {model,
## propos,
## learn,
## art,
## state} => {featur} 0.1000000 0.7500000 1.406250 3
## [25413] {featur,
## propos,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25414] {featur,
## model,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25415] {featur,
## model,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25416] {featur,
## model,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [25417] {featur,
## model,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [25418] {perform,
## propos,
## problem,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [25419] {model,
## perform,
## problem,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [25420] {model,
## perform,
## propos,
## problem,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [25421] {model,
## propos,
## problem,
## learn,
## art} => {perform} 0.1000000 1.0000000 2.142857 3
## [25422] {model,
## perform,
## propos,
## learn,
## art} => {problem} 0.1000000 1.0000000 3.333333 3
## [25423] {model,
## perform,
## propos,
## problem,
## learn} => {art} 0.1000000 1.0000000 7.500000 3
## [25424] {data,
## task,
## propos,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [25425] {data,
## model,
## task,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [25426] {data,
## model,
## task,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [25427] {model,
## task,
## propos,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [25428] {data,
## model,
## propos,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [25429] {data,
## model,
## task,
## propos,
## learn} => {art} 0.1000000 0.7500000 5.625000 3
## [25430] {data,
## task,
## propos,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [25431] {data,
## featur,
## task,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [25432] {data,
## featur,
## task,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [25433] {featur,
## task,
## propos,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [25434] {data,
## featur,
## propos,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [25435] {data,
## featur,
## task,
## propos,
## learn} => {art} 0.1000000 0.7500000 5.625000 3
## [25436] {data,
## model,
## task,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [25437] {data,
## featur,
## task,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [25438] {data,
## featur,
## model,
## task,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [25439] {featur,
## model,
## task,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [25440] {data,
## featur,
## model,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [25441] {data,
## model,
## task,
## propos,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [25442] {data,
## featur,
## task,
## propos,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [25443] {data,
## featur,
## model,
## task,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [25444] {featur,
## model,
## task,
## propos,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [25445] {data,
## featur,
## model,
## propos,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [25446] {data,
## featur,
## model,
## task,
## propos} => {art} 0.1000000 0.7500000 5.625000 3
## [25447] {model,
## task,
## propos,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [25448] {featur,
## task,
## propos,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [25449] {featur,
## model,
## task,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [25450] {featur,
## model,
## task,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [25451] {featur,
## model,
## propos,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [25452] {featur,
## model,
## task,
## propos,
## learn} => {art} 0.1000000 0.7500000 5.625000 3
## [25453] {data,
## model,
## propos,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [25454] {data,
## featur,
## propos,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [25455] {data,
## featur,
## model,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [25456] {data,
## featur,
## model,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [25457] {featur,
## model,
## propos,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [25458] {perform,
## propos,
## problem,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25459] {model,
## perform,
## problem,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25460] {model,
## perform,
## propos,
## problem,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25461] {model,
## propos,
## problem,
## learn,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [25462] {model,
## perform,
## propos,
## learn,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [25463] {model,
## perform,
## propos,
## problem,
## learn} => {state} 0.1000000 1.0000000 7.500000 3
## [25464] {data,
## task,
## propos,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25465] {data,
## model,
## task,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25466] {data,
## model,
## task,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25467] {model,
## task,
## propos,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25468] {data,
## model,
## propos,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25469] {data,
## model,
## task,
## propos,
## learn} => {state} 0.1000000 0.7500000 5.625000 3
## [25470] {data,
## task,
## propos,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25471] {data,
## featur,
## task,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25472] {data,
## featur,
## task,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25473] {featur,
## task,
## propos,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25474] {data,
## featur,
## propos,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25475] {data,
## featur,
## task,
## propos,
## learn} => {state} 0.1000000 0.7500000 5.625000 3
## [25476] {data,
## model,
## task,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25477] {data,
## featur,
## task,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25478] {data,
## featur,
## model,
## task,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25479] {featur,
## model,
## task,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25480] {data,
## featur,
## model,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25481] {data,
## model,
## task,
## propos,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25482] {data,
## featur,
## task,
## propos,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25483] {data,
## featur,
## model,
## task,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25484] {featur,
## model,
## task,
## propos,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25485] {data,
## featur,
## model,
## propos,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25486] {data,
## featur,
## model,
## task,
## propos} => {state} 0.1000000 0.7500000 5.625000 3
## [25487] {model,
## task,
## propos,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25488] {featur,
## task,
## propos,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25489] {featur,
## model,
## task,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25490] {featur,
## model,
## task,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25491] {featur,
## model,
## propos,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [25492] {featur,
## model,
## task,
## propos,
## learn} => {state} 0.1000000 0.7500000 5.625000 3
## [25493] {data,
## model,
## propos,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [25494] {data,
## featur,
## propos,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [25495] {data,
## featur,
## model,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [25496] {data,
## featur,
## model,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [25497] {featur,
## model,
## propos,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [25498] {approach,
## method,
## show,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [25499] {approach,
## method,
## dataset,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [25500] {approach,
## method,
## show,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25501] {method,
## show,
## dataset,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [25502] {approach,
## show,
## dataset,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [25503] {approach,
## method,
## show,
## dataset,
## propos} => {joint} 0.1000000 0.7500000 5.625000 3
## [25504] {approach,
## method,
## show,
## dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [25505] {approach,
## method,
## model,
## dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [25506] {approach,
## method,
## model,
## show,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25507] {method,
## model,
## show,
## dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [25508] {approach,
## model,
## show,
## dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [25509] {approach,
## method,
## model,
## show,
## dataset} => {joint} 0.1000000 0.7500000 5.625000 3
## [25510] {approach,
## method,
## dataset,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [25511] {approach,
## method,
## model,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [25512] {approach,
## method,
## model,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25513] {method,
## model,
## dataset,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [25514] {approach,
## model,
## dataset,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [25515] {approach,
## method,
## model,
## dataset,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [25516] {approach,
## method,
## show,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [25517] {approach,
## method,
## model,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [25518] {approach,
## method,
## model,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [25519] {method,
## model,
## show,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [25520] {approach,
## model,
## show,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [25521] {approach,
## method,
## model,
## show,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [25522] {method,
## show,
## dataset,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [25523] {method,
## model,
## show,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [25524] {method,
## model,
## dataset,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [25525] {method,
## model,
## show,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25526] {model,
## show,
## dataset,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [25527] {method,
## model,
## show,
## dataset,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [25528] {approach,
## show,
## dataset,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [25529] {approach,
## model,
## show,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [25530] {approach,
## model,
## dataset,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [25531] {approach,
## model,
## show,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25532] {model,
## show,
## dataset,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [25533] {approach,
## model,
## show,
## dataset,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [25534] {approach,
## evalu,
## method,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25535] {approach,
## evalu,
## featur,
## method,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [25536] {evalu,
## featur,
## method,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [25537] {approach,
## evalu,
## featur,
## method,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [25538] {approach,
## evalu,
## featur,
## represent,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [25539] {approach,
## featur,
## method,
## represent,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [25540] {approach,
## evalu,
## method,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [25541] {approach,
## evalu,
## method,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [25542] {evalu,
## method,
## network,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [25543] {approach,
## evalu,
## method,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [25544] {approach,
## evalu,
## network,
## represent,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [25545] {approach,
## method,
## network,
## represent,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [25546] {approach,
## evalu,
## featur,
## method,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [25547] {approach,
## evalu,
## method,
## network,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25548] {evalu,
## featur,
## method,
## network,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [25549] {approach,
## evalu,
## featur,
## method,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [25550] {approach,
## evalu,
## featur,
## network,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [25551] {approach,
## featur,
## method,
## network,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [25552] {evalu,
## featur,
## method,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [25553] {evalu,
## method,
## network,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25554] {evalu,
## featur,
## method,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [25555] {evalu,
## featur,
## method,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [25556] {evalu,
## featur,
## network,
## represent,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [25557] {featur,
## method,
## network,
## represent,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [25558] {approach,
## evalu,
## featur,
## method,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [25559] {approach,
## evalu,
## method,
## network,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [25560] {approach,
## evalu,
## featur,
## method,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [25561] {evalu,
## featur,
## method,
## network,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [25562] {approach,
## evalu,
## featur,
## network,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [25563] {approach,
## featur,
## method,
## network,
## represent} => {evalu} 0.1000000 1.0000000 6.000000 3
## [25564] {approach,
## evalu,
## featur,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [25565] {approach,
## evalu,
## network,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25566] {approach,
## evalu,
## featur,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [25567] {evalu,
## featur,
## network,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [25568] {approach,
## evalu,
## featur,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [25569] {approach,
## featur,
## network,
## represent,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [25570] {data,
## model,
## outperform,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [25571] {data,
## featur,
## outperform,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [25572] {data,
## featur,
## model,
## outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [25573] {featur,
## model,
## outperform,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [25574] {data,
## featur,
## model,
## outperform,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [25575] {data,
## featur,
## model,
## show,
## task} => {outperform} 0.1000000 0.7500000 5.625000 3
## [25576] {data,
## task,
## dataset,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [25577] {data,
## featur,
## task,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [25578] {data,
## featur,
## task,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25579] {featur,
## task,
## dataset,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [25580] {data,
## featur,
## dataset,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [25581] {data,
## featur,
## task,
## dataset,
## learn} => {design} 0.1000000 0.7500000 5.625000 3
## [25582] {data,
## task,
## dataset,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [25583] {data,
## network,
## task,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [25584] {data,
## network,
## task,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25585] {network,
## task,
## dataset,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [25586] {data,
## network,
## dataset,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [25587] {data,
## network,
## task,
## dataset,
## learn} => {design} 0.1000000 0.7500000 5.625000 3
## [25588] {data,
## featur,
## task,
## dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [25589] {data,
## network,
## task,
## dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [25590] {data,
## featur,
## network,
## task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25591] {featur,
## network,
## task,
## dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [25592] {data,
## featur,
## network,
## dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [25593] {data,
## featur,
## network,
## task,
## dataset} => {design} 0.1000000 1.0000000 7.500000 3
## [25594] {data,
## featur,
## task,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [25595] {data,
## network,
## task,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [25596] {data,
## featur,
## network,
## task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [25597] {featur,
## network,
## task,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [25598] {data,
## featur,
## network,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [25599] {data,
## featur,
## network,
## task,
## learn} => {design} 0.1000000 1.0000000 7.500000 3
## [25600] {featur,
## task,
## dataset,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [25601] {network,
## task,
## dataset,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [25602] {featur,
## network,
## task,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [25603] {featur,
## network,
## task,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25604] {featur,
## network,
## dataset,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [25605] {featur,
## network,
## task,
## dataset,
## learn} => {design} 0.1000000 1.0000000 7.500000 3
## [25606] {data,
## featur,
## dataset,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [25607] {data,
## network,
## dataset,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [25608] {data,
## featur,
## network,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [25609] {data,
## featur,
## network,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25610] {featur,
## network,
## dataset,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [25611] {data,
## featur,
## network,
## dataset,
## learn} => {design} 0.1000000 1.0000000 7.500000 3
## [25612] {method,
## appli,
## detect,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [25613] {featur,
## method,
## appli,
## detect,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [25614] {featur,
## method,
## appli,
## detect,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [25615] {featur,
## appli,
## detect,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [25616] {featur,
## method,
## detect,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [25617] {featur,
## method,
## appli,
## perform,
## propos} => {detect} 0.1000000 1.0000000 6.000000 3
## [25618] {method,
## show,
## detect,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [25619] {featur,
## method,
## show,
## detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [25620] {featur,
## method,
## detect,
## object,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [25621] {featur,
## show,
## detect,
## object,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [25622] {featur,
## method,
## show,
## detect,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [25623] {featur,
## method,
## show,
## object,
## propos} => {detect} 0.1000000 1.0000000 6.000000 3
## [25624] {featur,
## architectur,
## dataset,
## detect,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [25625] {network,
## architectur,
## dataset,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [25626] {featur,
## network,
## architectur,
## detect,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25627] {featur,
## network,
## architectur,
## dataset,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [25628] {featur,
## network,
## dataset,
## detect,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [25629] {featur,
## network,
## architectur,
## dataset,
## perform} => {detect} 0.1000000 1.0000000 6.000000 3
## [25630] {featur,
## method,
## dataset,
## detect,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [25631] {method,
## network,
## dataset,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [25632] {featur,
## method,
## network,
## dataset,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [25633] {featur,
## method,
## network,
## detect,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25634] {featur,
## network,
## dataset,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [25635] {featur,
## method,
## network,
## dataset,
## propos} => {detect} 0.1000000 1.0000000 6.000000 3
## [25636] {architectur,
## dataset,
## addit,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [25637] {architectur,
## propos,
## addit,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25638] {architectur,
## dataset,
## propos,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [25639] {dataset,
## propos,
## addit,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [25640] {architectur,
## dataset,
## propos,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [25641] {architectur,
## dataset,
## propos,
## addit,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [25642] {architectur,
## dataset,
## addit,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [25643] {network,
## architectur,
## addit,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25644] {network,
## architectur,
## dataset,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [25645] {network,
## dataset,
## addit,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [25646] {network,
## architectur,
## dataset,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [25647] {network,
## architectur,
## dataset,
## addit,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [25648] {architectur,
## propos,
## addit,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [25649] {network,
## architectur,
## addit,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [25650] {network,
## architectur,
## propos,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [25651] {network,
## propos,
## addit,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [25652] {network,
## architectur,
## propos,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [25653] {network,
## architectur,
## propos,
## addit,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [25654] {architectur,
## dataset,
## propos,
## addit,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [25655] {network,
## architectur,
## dataset,
## addit,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [25656] {network,
## architectur,
## propos,
## addit,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25657] {network,
## dataset,
## propos,
## addit,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [25658] {network,
## architectur,
## dataset,
## propos,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [25659] {network,
## architectur,
## dataset,
## propos,
## addit} => {effici} 0.1000000 1.0000000 6.000000 3
## [25660] {dataset,
## propos,
## addit,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [25661] {network,
## dataset,
## addit,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [25662] {network,
## propos,
## addit,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25663] {network,
## dataset,
## propos,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [25664] {network,
## dataset,
## propos,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [25665] {network,
## dataset,
## propos,
## addit,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [25666] {reduc,
## comput,
## effici,
## optim,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [25667] {reduc,
## improv,
## comput,
## effici,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [25668] {reduc,
## improv,
## comput,
## effici,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [25669] {improv,
## comput,
## effici,
## optim,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [25670] {reduc,
## improv,
## effici,
## optim,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [25671] {reduc,
## improv,
## comput,
## optim,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [25672] {architectur,
## dataset,
## propos,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [25673] {network,
## architectur,
## dataset,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [25674] {network,
## architectur,
## propos,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25675] {network,
## architectur,
## dataset,
## propos,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [25676] {network,
## dataset,
## propos,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [25677] {network,
## architectur,
## dataset,
## propos,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [25678] {dataset,
## propos,
## addit,
## set,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [25679] {network,
## dataset,
## addit,
## set,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [25680] {network,
## propos,
## addit,
## set,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25681] {network,
## dataset,
## propos,
## addit,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [25682] {network,
## dataset,
## propos,
## set,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [25683] {network,
## dataset,
## propos,
## addit,
## work} => {set} 0.1000000 0.7500000 5.625000 3
## [25684] {featur,
## architectur,
## neural,
## result,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [25685] {network,
## architectur,
## neural,
## result,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [25686] {featur,
## network,
## architectur,
## result,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [25687] {featur,
## network,
## architectur,
## neural,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [25688] {featur,
## network,
## neural,
## result,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [25689] {featur,
## network,
## architectur,
## neural,
## result} => {shallow} 0.1000000 1.0000000 6.000000 3
## [25690] {approach,
## data,
## task,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25691] {approach,
## task,
## dataset,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25692] {approach,
## data,
## task,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25693] {data,
## task,
## dataset,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25694] {approach,
## data,
## dataset,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25695] {approach,
## data,
## task,
## dataset,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25696] {approach,
## data,
## task,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25697] {approach,
## task,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25698] {approach,
## data,
## task,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25699] {data,
## task,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25700] {approach,
## data,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25701] {approach,
## data,
## task,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25702] {approach,
## data,
## task,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25703] {approach,
## represent,
## task,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25704] {approach,
## data,
## represent,
## task,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25705] {data,
## represent,
## task,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25706] {approach,
## data,
## represent,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25707] {approach,
## data,
## represent,
## task,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25708] {approach,
## data,
## task,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25709] {approach,
## task,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25710] {approach,
## data,
## task,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25711] {data,
## task,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25712] {approach,
## data,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25713] {approach,
## data,
## task,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25714] {approach,
## task,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25715] {approach,
## task,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25716] {approach,
## task,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25717] {task,
## dataset,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25718] {approach,
## dataset,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25719] {approach,
## task,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25720] {approach,
## task,
## dataset,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25721] {approach,
## represent,
## task,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25722] {approach,
## represent,
## task,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25723] {represent,
## task,
## dataset,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25724] {approach,
## represent,
## dataset,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25725] {approach,
## represent,
## task,
## dataset,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25726] {approach,
## task,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25727] {approach,
## task,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25728] {approach,
## task,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25729] {task,
## dataset,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25730] {approach,
## dataset,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25731] {approach,
## task,
## dataset,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25732] {approach,
## task,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25733] {approach,
## represent,
## task,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25734] {approach,
## represent,
## task,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25735] {represent,
## task,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25736] {approach,
## represent,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25737] {approach,
## represent,
## task,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25738] {approach,
## task,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25739] {approach,
## task,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25740] {approach,
## task,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25741] {task,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25742] {approach,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25743] {approach,
## task,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25744] {approach,
## represent,
## task,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25745] {approach,
## task,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25746] {approach,
## represent,
## task,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25747] {represent,
## task,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25748] {approach,
## represent,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25749] {approach,
## represent,
## task,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25750] {approach,
## data,
## task,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25751] {approach,
## data,
## task,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25752] {approach,
## task,
## dataset,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25753] {data,
## task,
## dataset,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25754] {approach,
## data,
## dataset,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25755] {approach,
## data,
## task,
## dataset,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25756] {approach,
## data,
## task,
## dataset,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25757] {approach,
## data,
## represent,
## task,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25758] {approach,
## represent,
## task,
## dataset,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25759] {data,
## represent,
## task,
## dataset,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25760] {approach,
## data,
## represent,
## dataset,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25761] {approach,
## data,
## represent,
## task,
## dataset} => {semant} 0.1000000 1.0000000 6.000000 3
## [25762] {approach,
## data,
## task,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25763] {approach,
## data,
## task,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25764] {approach,
## task,
## dataset,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25765] {data,
## task,
## dataset,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25766] {approach,
## data,
## dataset,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25767] {approach,
## data,
## task,
## dataset,
## propos} => {semant} 0.1000000 1.0000000 6.000000 3
## [25768] {approach,
## data,
## task,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25769] {approach,
## data,
## represent,
## task,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25770] {approach,
## represent,
## task,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25771] {data,
## represent,
## task,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25772] {approach,
## data,
## represent,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25773] {approach,
## data,
## represent,
## task,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25774] {approach,
## data,
## task,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25775] {approach,
## data,
## task,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25776] {approach,
## task,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25777] {data,
## task,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25778] {approach,
## data,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25779] {approach,
## data,
## task,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25780] {approach,
## data,
## represent,
## task,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25781] {approach,
## data,
## task,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25782] {approach,
## represent,
## task,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25783] {data,
## represent,
## task,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25784] {approach,
## data,
## represent,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25785] {approach,
## data,
## represent,
## task,
## propos} => {semant} 0.1000000 0.7500000 4.500000 3
## [25786] {approach,
## task,
## dataset,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25787] {approach,
## represent,
## task,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25788] {approach,
## represent,
## task,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25789] {represent,
## task,
## dataset,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25790] {approach,
## represent,
## dataset,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25791] {approach,
## represent,
## task,
## dataset,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25792] {approach,
## task,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25793] {approach,
## task,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25794] {approach,
## task,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25795] {task,
## dataset,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25796] {approach,
## dataset,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25797] {approach,
## task,
## dataset,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25798] {approach,
## represent,
## task,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25799] {approach,
## task,
## dataset,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25800] {approach,
## represent,
## task,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25801] {represent,
## task,
## dataset,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25802] {approach,
## represent,
## dataset,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25803] {approach,
## represent,
## task,
## dataset,
## propos} => {semant} 0.1000000 1.0000000 6.000000 3
## [25804] {approach,
## represent,
## task,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25805] {approach,
## task,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25806] {approach,
## represent,
## task,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25807] {represent,
## task,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25808] {approach,
## represent,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25809] {approach,
## represent,
## task,
## propos,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [25810] {data,
## task,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25811] {data,
## task,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25812] {task,
## dataset,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25813] {data,
## task,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25814] {data,
## dataset,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25815] {data,
## task,
## dataset,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [25816] {data,
## task,
## dataset,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25817] {data,
## represent,
## task,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25818] {represent,
## task,
## dataset,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25819] {data,
## represent,
## task,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25820] {data,
## represent,
## dataset,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25821] {data,
## represent,
## task,
## dataset,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25822] {data,
## task,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25823] {data,
## task,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25824] {task,
## dataset,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25825] {data,
## task,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25826] {data,
## dataset,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25827] {data,
## task,
## dataset,
## propos,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [25828] {data,
## task,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25829] {data,
## represent,
## task,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25830] {represent,
## task,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25831] {data,
## represent,
## task,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25832] {data,
## represent,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25833] {data,
## represent,
## task,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25834] {data,
## task,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25835] {data,
## task,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25836] {task,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25837] {data,
## task,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25838] {data,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25839] {data,
## task,
## propos,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [25840] {data,
## represent,
## task,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25841] {data,
## task,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25842] {represent,
## task,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25843] {data,
## represent,
## task,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25844] {data,
## represent,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25845] {data,
## represent,
## task,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25846] {task,
## dataset,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25847] {represent,
## task,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25848] {represent,
## task,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25849] {represent,
## task,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25850] {represent,
## dataset,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25851] {represent,
## task,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25852] {task,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25853] {task,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25854] {task,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25855] {task,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25856] {dataset,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25857] {task,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [25858] {represent,
## task,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25859] {task,
## dataset,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25860] {represent,
## task,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25861] {represent,
## task,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25862] {represent,
## dataset,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25863] {represent,
## task,
## dataset,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25864] {represent,
## task,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25865] {task,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25866] {represent,
## task,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25867] {represent,
## task,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25868] {represent,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25869] {represent,
## task,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25870] {data,
## task,
## dataset,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25871] {data,
## represent,
## task,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25872] {data,
## represent,
## task,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25873] {represent,
## task,
## dataset,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25874] {data,
## represent,
## dataset,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25875] {data,
## represent,
## task,
## dataset,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [25876] {data,
## task,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25877] {data,
## task,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25878] {data,
## task,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25879] {task,
## dataset,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25880] {data,
## dataset,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25881] {data,
## task,
## dataset,
## propos,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [25882] {data,
## represent,
## task,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25883] {data,
## task,
## dataset,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25884] {data,
## represent,
## task,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25885] {represent,
## task,
## dataset,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25886] {data,
## represent,
## dataset,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25887] {data,
## represent,
## task,
## dataset,
## propos} => {semant} 0.1000000 1.0000000 6.000000 3
## [25888] {data,
## represent,
## task,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25889] {data,
## task,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25890] {data,
## represent,
## task,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25891] {represent,
## task,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25892] {data,
## represent,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25893] {data,
## represent,
## task,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25894] {represent,
## task,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25895] {task,
## dataset,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25896] {represent,
## task,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25897] {represent,
## task,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25898] {represent,
## dataset,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [25899] {represent,
## task,
## dataset,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25900] {approach,
## data,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25901] {approach,
## data,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25902] {approach,
## dataset,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25903] {approach,
## data,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25904] {data,
## dataset,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25905] {approach,
## data,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25906] {approach,
## data,
## dataset,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25907] {approach,
## data,
## represent,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25908] {approach,
## represent,
## dataset,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25909] {approach,
## data,
## represent,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25910] {data,
## represent,
## dataset,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25911] {approach,
## data,
## represent,
## dataset,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25912] {approach,
## data,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25913] {approach,
## data,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25914] {approach,
## dataset,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25915] {approach,
## data,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25916] {data,
## dataset,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25917] {approach,
## data,
## dataset,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25918] {approach,
## data,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25919] {approach,
## data,
## represent,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25920] {approach,
## represent,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25921] {approach,
## data,
## represent,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25922] {data,
## represent,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25923] {approach,
## data,
## represent,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25924] {approach,
## data,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25925] {approach,
## data,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25926] {approach,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25927] {approach,
## data,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25928] {data,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25929] {approach,
## data,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25930] {approach,
## data,
## represent,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25931] {approach,
## data,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25932] {approach,
## represent,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25933] {approach,
## data,
## represent,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25934] {data,
## represent,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25935] {approach,
## data,
## represent,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25936] {approach,
## dataset,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25937] {approach,
## represent,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25938] {approach,
## represent,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25939] {approach,
## represent,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25940] {represent,
## dataset,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25941] {approach,
## represent,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25942] {approach,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25943] {approach,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25944] {approach,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25945] {approach,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25946] {dataset,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25947] {approach,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25948] {approach,
## represent,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25949] {approach,
## dataset,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25950] {approach,
## represent,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25951] {approach,
## represent,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25952] {represent,
## dataset,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25953] {approach,
## represent,
## dataset,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [25954] {approach,
## represent,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25955] {approach,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25956] {approach,
## represent,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25957] {approach,
## represent,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25958] {represent,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25959] {approach,
## represent,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25960] {approach,
## data,
## dataset,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25961] {approach,
## data,
## represent,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25962] {approach,
## data,
## represent,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25963] {approach,
## represent,
## dataset,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25964] {data,
## represent,
## dataset,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25965] {approach,
## data,
## represent,
## dataset,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25966] {approach,
## data,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25967] {approach,
## data,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25968] {approach,
## data,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25969] {approach,
## dataset,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25970] {data,
## dataset,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25971] {approach,
## data,
## dataset,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25972] {approach,
## data,
## represent,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25973] {approach,
## data,
## dataset,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25974] {approach,
## data,
## represent,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25975] {approach,
## represent,
## dataset,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25976] {data,
## represent,
## dataset,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25977] {approach,
## data,
## represent,
## dataset,
## propos} => {semant} 0.1000000 1.0000000 6.000000 3
## [25978] {approach,
## data,
## represent,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25979] {approach,
## data,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25980] {approach,
## data,
## represent,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25981] {approach,
## represent,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25982] {data,
## represent,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25983] {approach,
## data,
## represent,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25984] {approach,
## represent,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25985] {approach,
## dataset,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25986] {approach,
## represent,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25987] {approach,
## represent,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25988] {represent,
## dataset,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [25989] {approach,
## represent,
## dataset,
## propos,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [25990] {data,
## dataset,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [25991] {data,
## represent,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25992] {data,
## represent,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25993] {represent,
## dataset,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [25994] {data,
## represent,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [25995] {data,
## represent,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [25996] {data,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [25997] {data,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [25998] {data,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [25999] {dataset,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [26000] {data,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [26001] {data,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [26002] {data,
## represent,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [26003] {data,
## dataset,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [26004] {data,
## represent,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26005] {represent,
## dataset,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [26006] {data,
## represent,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [26007] {data,
## represent,
## dataset,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [26008] {data,
## represent,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [26009] {data,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [26010] {data,
## represent,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [26011] {represent,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [26012] {data,
## represent,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [26013] {data,
## represent,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [26014] {represent,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [26015] {dataset,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [26016] {represent,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [26017] {represent,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26018] {represent,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [26019] {represent,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [26020] {data,
## represent,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [26021] {data,
## dataset,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [26022] {data,
## represent,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [26023] {data,
## represent,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26024] {represent,
## dataset,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [26025] {data,
## represent,
## dataset,
## propos,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [26026] {architectur,
## dataset,
## process,
## analysi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [26027] {network,
## architectur,
## process,
## analysi,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26028] {network,
## architectur,
## dataset,
## process,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [26029] {network,
## dataset,
## process,
## analysi,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26030] {network,
## architectur,
## dataset,
## analysi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [26031] {network,
## architectur,
## dataset,
## process,
## work} => {analysi} 0.1000000 1.0000000 7.500000 3
## [26032] {classif,
## architectur,
## experi,
## propos,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [26033] {classif,
## network,
## architectur,
## experi,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [26034] {network,
## architectur,
## experi,
## propos,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [26035] {classif,
## network,
## experi,
## propos,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26036] {classif,
## network,
## architectur,
## propos,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [26037] {classif,
## network,
## architectur,
## experi,
## propos} => {analysi} 0.1000000 0.7500000 5.625000 3
## [26038] {appli,
## dataset,
## propos,
## addit,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [26039] {network,
## appli,
## dataset,
## addit,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [26040] {network,
## appli,
## propos,
## addit,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26041] {network,
## appli,
## dataset,
## propos,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [26042] {network,
## dataset,
## propos,
## addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [26043] {network,
## appli,
## dataset,
## propos,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [26044] {architectur,
## dataset,
## propos,
## addit,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [26045] {network,
## architectur,
## dataset,
## addit,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [26046] {network,
## architectur,
## propos,
## addit,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26047] {network,
## architectur,
## dataset,
## propos,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [26048] {network,
## dataset,
## propos,
## addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [26049] {network,
## architectur,
## dataset,
## propos,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [26050] {machin,
## show,
## problem,
## recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [26051] {machin,
## model,
## problem,
## recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [26052] {machin,
## model,
## show,
## recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [26053] {model,
## show,
## problem,
## recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [26054] {machin,
## model,
## show,
## problem,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [26055] {machin,
## model,
## show,
## problem,
## recent} => {function} 0.1000000 1.0000000 6.000000 3
## [26056] {data,
## task,
## object,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [26057] {task,
## object,
## propos,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [26058] {data,
## object,
## propos,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [26059] {data,
## task,
## propos,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [26060] {data,
## task,
## object,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [26061] {data,
## task,
## object,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [26062] {data,
## task,
## object,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [26063] {featur,
## task,
## object,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [26064] {data,
## featur,
## object,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [26065] {data,
## featur,
## task,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [26066] {data,
## featur,
## task,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [26067] {data,
## featur,
## task,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [26068] {task,
## object,
## propos,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [26069] {featur,
## task,
## object,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [26070] {featur,
## object,
## propos,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [26071] {featur,
## task,
## propos,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [26072] {featur,
## task,
## object,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [26073] {featur,
## task,
## object,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [26074] {data,
## object,
## propos,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [26075] {data,
## featur,
## object,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [26076] {featur,
## object,
## propos,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [26077] {data,
## featur,
## propos,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [26078] {data,
## featur,
## object,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [26079] {data,
## featur,
## object,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [26080] {data,
## task,
## propos,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [26081] {data,
## featur,
## task,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [26082] {featur,
## task,
## propos,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [26083] {data,
## featur,
## propos,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [26084] {data,
## featur,
## task,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [26085] {data,
## featur,
## task,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [26086] {data,
## task,
## object,
## propos,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [26087] {data,
## featur,
## task,
## object,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [26088] {featur,
## task,
## object,
## propos,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [26089] {data,
## featur,
## object,
## propos,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [26090] {data,
## featur,
## task,
## propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [26091] {data,
## featur,
## task,
## object,
## propos} => {function} 0.1000000 0.7500000 4.500000 3
## [26092] {paper,
## train,
## accuraci,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [26093] {paper,
## represent,
## train,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [26094] {paper,
## represent,
## accuraci,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [26095] {represent,
## train,
## accuraci,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [26096] {paper,
## represent,
## train,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26097] {paper,
## represent,
## train,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [26098] {paper,
## train,
## accuraci,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [26099] {featur,
## paper,
## train,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [26100] {featur,
## paper,
## accuraci,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [26101] {featur,
## train,
## accuraci,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [26102] {featur,
## paper,
## train,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26103] {featur,
## paper,
## train,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [26104] {paper,
## represent,
## train,
## accuraci,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [26105] {featur,
## paper,
## train,
## accuraci,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [26106] {featur,
## paper,
## represent,
## accuraci,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [26107] {featur,
## represent,
## train,
## accuraci,
## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [26108] {featur,
## paper,
## represent,
## train,
## accuraci} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26109] {featur,
## paper,
## represent,
## train,
## achiev} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [26110] {paper,
## represent,
## accuraci,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [26111] {featur,
## paper,
## accuraci,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [26112] {featur,
## paper,
## represent,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [26113] {featur,
## represent,
## accuraci,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [26114] {featur,
## paper,
## represent,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26115] {featur,
## paper,
## represent,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [26116] {represent,
## train,
## accuraci,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [26117] {featur,
## train,
## accuraci,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [26118] {featur,
## represent,
## train,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [26119] {featur,
## represent,
## accuraci,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [26120] {featur,
## represent,
## train,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26121] {featur,
## represent,
## train,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [26122] {paper,
## represent,
## train,
## accuraci,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [26123] {featur,
## paper,
## train,
## accuraci,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [26124] {featur,
## paper,
## represent,
## train,
## accuraci} => {learn} 0.1000000 1.0000000 2.307692 3
## [26125] {featur,
## paper,
## represent,
## accuraci,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [26126] {featur,
## represent,
## train,
## accuraci,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [26127] {featur,
## paper,
## represent,
## train,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [26128] {approach,
## train,
## neural,
## propos,
## base} => {network} 0.1000000 1.0000000 1.578947 3
## [26129] {approach,
## network,
## train,
## neural,
## base} => {propos} 0.1000000 1.0000000 2.000000 3
## [26130] {network,
## train,
## neural,
## propos,
## base} => {approach} 0.1000000 1.0000000 2.500000 3
## [26131] {approach,
## network,
## neural,
## propos,
## base} => {train} 0.1000000 1.0000000 2.500000 3
## [26132] {approach,
## network,
## train,
## propos,
## base} => {neural} 0.1000000 1.0000000 3.000000 3
## [26133] {approach,
## network,
## train,
## neural,
## propos} => {base} 0.1000000 0.7500000 4.500000 3
## [26134] {train,
## neural,
## result,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26135] {dataset,
## neural,
## result,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [26136] {train,
## dataset,
## result,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [26137] {train,
## dataset,
## neural,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [26138] {train,
## dataset,
## neural,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [26139] {train,
## dataset,
## neural,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [26140] {train,
## neural,
## result,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [26141] {network,
## neural,
## result,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [26142] {network,
## train,
## result,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [26143] {network,
## train,
## neural,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [26144] {network,
## train,
## neural,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [26145] {network,
## train,
## neural,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [26146] {dataset,
## neural,
## result,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [26147] {network,
## neural,
## result,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26148] {network,
## dataset,
## result,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [26149] {network,
## dataset,
## neural,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [26150] {network,
## dataset,
## neural,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [26151] {network,
## dataset,
## neural,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [26152] {train,
## dataset,
## result,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [26153] {network,
## train,
## result,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26154] {network,
## dataset,
## result,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [26155] {network,
## train,
## dataset,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [26156] {network,
## train,
## dataset,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [26157] {network,
## train,
## dataset,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [26158] {train,
## dataset,
## neural,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [26159] {network,
## train,
## neural,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26160] {network,
## dataset,
## neural,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [26161] {network,
## train,
## dataset,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [26162] {network,
## train,
## dataset,
## neural,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [26163] {network,
## train,
## dataset,
## neural,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [26164] {train,
## dataset,
## neural,
## result,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [26165] {network,
## train,
## neural,
## result,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26166] {network,
## dataset,
## neural,
## result,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [26167] {network,
## train,
## dataset,
## result,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [26168] {network,
## train,
## dataset,
## neural,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [26169] {network,
## train,
## dataset,
## neural,
## result} => {potenti} 0.1000000 1.0000000 6.000000 3
## [26170] {approach,
## make,
## perform,
## problem,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [26171] {approach,
## featur,
## make,
## problem,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [26172] {featur,
## make,
## perform,
## problem,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [26173] {approach,
## featur,
## make,
## perform,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [26174] {approach,
## featur,
## perform,
## problem,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [26175] {approach,
## featur,
## make,
## perform,
## problem} => {solv} 0.1000000 1.0000000 5.000000 3
## [26176] {train,
## result,
## work,
## layer,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [26177] {network,
## train,
## result,
## layer,
## specif} => {work} 0.1000000 1.0000000 2.500000 3
## [26178] {network,
## result,
## work,
## layer,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [26179] {network,
## train,
## work,
## layer,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [26180] {network,
## train,
## result,
## work,
## specif} => {layer} 0.1000000 1.0000000 5.000000 3
## [26181] {network,
## train,
## result,
## work,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [26182] {train,
## dataset,
## neural,
## result,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [26183] {network,
## train,
## neural,
## result,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26184] {network,
## dataset,
## neural,
## result,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [26185] {network,
## train,
## dataset,
## result,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [26186] {network,
## train,
## dataset,
## neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [26187] {network,
## train,
## dataset,
## neural,
## result} => {specif} 0.1000000 1.0000000 6.000000 3
## [26188] {method,
## appli,
## architectur,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [26189] {method,
## appli,
## architectur,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26190] {method,
## appli,
## architectur,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [26191] {appli,
## architectur,
## dataset,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26192] {method,
## appli,
## dataset,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26193] {method,
## architectur,
## dataset,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [26194] {method,
## appli,
## architectur,
## dataset,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [26195] {method,
## network,
## appli,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26196] {method,
## network,
## appli,
## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [26197] {network,
## appli,
## architectur,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [26198] {method,
## network,
## appli,
## dataset,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26199] {method,
## network,
## architectur,
## dataset,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [26200] {method,
## appli,
## architectur,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26201] {method,
## network,
## appli,
## architectur,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [26202] {method,
## network,
## appli,
## architectur,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [26203] {network,
## appli,
## architectur,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26204] {method,
## network,
## appli,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26205] {method,
## network,
## architectur,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [26206] {method,
## appli,
## architectur,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26207] {method,
## network,
## appli,
## architectur,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [26208] {method,
## network,
## appli,
## architectur,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26209] {network,
## appli,
## architectur,
## dataset,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26210] {method,
## network,
## appli,
## dataset,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26211] {method,
## network,
## architectur,
## dataset,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [26212] {appli,
## architectur,
## dataset,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26213] {network,
## appli,
## architectur,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [26214] {network,
## appli,
## architectur,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26215] {network,
## appli,
## architectur,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [26216] {network,
## appli,
## dataset,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26217] {network,
## architectur,
## dataset,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [26218] {classif,
## method,
## show,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [26219] {classif,
## method,
## appli,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [26220] {classif,
## method,
## show,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [26221] {classif,
## show,
## appli,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26222] {method,
## show,
## appli,
## perform,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26223] {classif,
## method,
## show,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [26224] {approach,
## show,
## appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26225] {approach,
## appli,
## dataset,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [26226] {approach,
## show,
## appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26227] {show,
## appli,
## dataset,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26228] {approach,
## show,
## appli,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26229] {approach,
## show,
## dataset,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [26230] {approach,
## show,
## appli,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [26231] {approach,
## network,
## appli,
## dataset,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [26232] {approach,
## network,
## show,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26233] {network,
## show,
## appli,
## dataset,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [26234] {approach,
## network,
## show,
## appli,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [26235] {approach,
## network,
## show,
## dataset,
## neural} => {appli} 0.1000000 1.0000000 5.000000 3
## [26236] {approach,
## appli,
## dataset,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26237] {approach,
## network,
## appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26238] {approach,
## network,
## appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26239] {network,
## appli,
## dataset,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26240] {approach,
## network,
## appli,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26241] {approach,
## network,
## dataset,
## neural,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [26242] {approach,
## show,
## appli,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26243] {approach,
## network,
## show,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26244] {approach,
## network,
## appli,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [26245] {network,
## show,
## appli,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26246] {approach,
## network,
## show,
## appli,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26247] {approach,
## network,
## show,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [26248] {show,
## appli,
## dataset,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26249] {network,
## show,
## appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26250] {network,
## appli,
## dataset,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [26251] {network,
## show,
## appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26252] {network,
## show,
## appli,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26253] {network,
## show,
## dataset,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [26254] {method,
## appli,
## dataset,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26255] {method,
## network,
## appli,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [26256] {method,
## network,
## appli,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26257] {method,
## network,
## appli,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [26258] {network,
## appli,
## dataset,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26259] {method,
## network,
## dataset,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [26260] {approach,
## show,
## appli,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26261] {approach,
## network,
## show,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [26262] {approach,
## network,
## appli,
## dataset,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [26263] {approach,
## network,
## show,
## appli,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26264] {network,
## show,
## appli,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26265] {approach,
## network,
## show,
## dataset,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [26266] {data,
## represent,
## dataset,
## work,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [26267] {data,
## featur,
## dataset,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [26268] {data,
## featur,
## represent,
## work,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26269] {featur,
## represent,
## dataset,
## work,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [26270] {data,
## featur,
## represent,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [26271] {data,
## featur,
## represent,
## dataset,
## work} => {larg} 0.1000000 1.0000000 6.000000 3
## [26272] {data,
## achiev,
## dataset,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [26273] {data,
## represent,
## achiev,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [26274] {data,
## represent,
## achiev,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26275] {represent,
## achiev,
## dataset,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [26276] {data,
## represent,
## dataset,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26277] {data,
## represent,
## achiev,
## dataset,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [26278] {data,
## achiev,
## dataset,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [26279] {data,
## featur,
## achiev,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [26280] {data,
## featur,
## achiev,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26281] {featur,
## achiev,
## dataset,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [26282] {data,
## featur,
## dataset,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26283] {data,
## featur,
## achiev,
## dataset,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [26284] {data,
## represent,
## achiev,
## dataset,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [26285] {data,
## featur,
## achiev,
## dataset,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [26286] {data,
## featur,
## represent,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26287] {featur,
## represent,
## achiev,
## dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [26288] {data,
## featur,
## represent,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26289] {data,
## featur,
## represent,
## achiev,
## dataset} => {challeng} 0.1000000 1.0000000 6.000000 3
## [26290] {data,
## represent,
## achiev,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [26291] {data,
## featur,
## achiev,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [26292] {data,
## featur,
## represent,
## achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [26293] {featur,
## represent,
## achiev,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [26294] {data,
## featur,
## represent,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26295] {data,
## featur,
## represent,
## achiev,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [26296] {represent,
## achiev,
## dataset,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [26297] {featur,
## achiev,
## dataset,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [26298] {featur,
## represent,
## achiev,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [26299] {featur,
## represent,
## achiev,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26300] {featur,
## represent,
## dataset,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26301] {featur,
## represent,
## achiev,
## dataset,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [26302] {data,
## paper,
## train,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [26303] {data,
## featur,
## paper,
## train,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [26304] {featur,
## paper,
## train,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [26305] {data,
## featur,
## paper,
## learn,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [26306] {data,
## featur,
## train,
## learn,
## challeng} => {paper} 0.1000000 1.0000000 3.000000 3
## [26307] {data,
## featur,
## paper,
## train,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [26308] {data,
## show,
## task,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [26309] {data,
## featur,
## task,
## learn,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [26310] {data,
## featur,
## show,
## task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [26311] {featur,
## show,
## task,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [26312] {data,
## featur,
## show,
## learn,
## challeng} => {task} 0.1000000 1.0000000 2.727273 3
## [26313] {data,
## featur,
## show,
## task,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [26314] {data,
## represent,
## dataset,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [26315] {data,
## featur,
## dataset,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [26316] {data,
## featur,
## represent,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [26317] {data,
## featur,
## represent,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26318] {featur,
## represent,
## dataset,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [26319] {data,
## featur,
## represent,
## dataset,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [26320] {data,
## model,
## propos,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [26321] {data,
## featur,
## propos,
## learn,
## challeng} => {model} 0.1000000 1.0000000 1.875000 3
## [26322] {data,
## featur,
## model,
## learn,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [26323] {data,
## featur,
## model,
## propos,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [26324] {featur,
## model,
## propos,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [26325] {approach,
## complex,
## input,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [26326] {approach,
## complex,
## featur,
## input,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [26327] {approach,
## complex,
## featur,
## input,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [26328] {complex,
## featur,
## input,
## model,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [26329] {approach,
## complex,
## featur,
## model,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [26330] {approach,
## featur,
## input,
## model,
## show} => {complex} 0.1000000 1.0000000 5.000000 3
## [26331] {paper,
## task,
## train,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [26332] {paper,
## task,
## learn,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [26333] {paper,
## train,
## learn,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [26334] {task,
## train,
## learn,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [26335] {paper,
## task,
## train,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [26336] {paper,
## task,
## train,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [26337] {paper,
## task,
## train,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [26338] {featur,
## paper,
## task,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [26339] {featur,
## paper,
## train,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [26340] {featur,
## task,
## train,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [26341] {featur,
## paper,
## task,
## train,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [26342] {featur,
## paper,
## task,
## train,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [26343] {paper,
## task,
## learn,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [26344] {featur,
## paper,
## task,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [26345] {featur,
## paper,
## learn,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [26346] {featur,
## task,
## learn,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [26347] {featur,
## paper,
## task,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [26348] {featur,
## paper,
## task,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [26349] {paper,
## train,
## learn,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [26350] {featur,
## paper,
## train,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [26351] {featur,
## paper,
## learn,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [26352] {featur,
## train,
## learn,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [26353] {featur,
## paper,
## train,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [26354] {featur,
## paper,
## train,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [26355] {task,
## train,
## learn,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [26356] {featur,
## task,
## train,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [26357] {featur,
## task,
## learn,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [26358] {featur,
## train,
## learn,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [26359] {featur,
## task,
## train,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [26360] {featur,
## task,
## train,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [26361] {method,
## represent,
## learn,
## effect,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [26362] {method,
## propos,
## learn,
## effect,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [26363] {method,
## represent,
## propos,
## effect,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [26364] {represent,
## propos,
## learn,
## effect,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [26365] {method,
## represent,
## propos,
## learn,
## effect} => {success} 0.1000000 1.0000000 3.750000 3
## [26366] {method,
## represent,
## propos,
## learn,
## success} => {effect} 0.1000000 0.7500000 3.214286 3
## [26367] {show,
## perform,
## problem,
## learn,
## effect} => {propos} 0.1000000 1.0000000 2.000000 3
## [26368] {perform,
## propos,
## problem,
## learn,
## effect} => {show} 0.1000000 1.0000000 1.875000 3
## [26369] {show,
## perform,
## propos,
## problem,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [26370] {show,
## propos,
## problem,
## learn,
## effect} => {perform} 0.1000000 1.0000000 2.142857 3
## [26371] {show,
## perform,
## propos,
## learn,
## effect} => {problem} 0.1000000 1.0000000 3.333333 3
## [26372] {show,
## perform,
## propos,
## problem,
## learn} => {effect} 0.1000000 1.0000000 4.285714 3
## [26373] {paper,
## task,
## train,
## learn,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [26374] {featur,
## paper,
## task,
## train,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [26375] {featur,
## paper,
## task,
## learn,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [26376] {featur,
## paper,
## train,
## learn,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [26377] {featur,
## task,
## train,
## learn,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [26378] {featur,
## paper,
## task,
## train,
## learn} => {effect} 0.1000000 1.0000000 4.285714 3
## [26379] {represent,
## perform,
## problem,
## applic,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [26380] {perform,
## propos,
## problem,
## applic,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [26381] {represent,
## propos,
## problem,
## applic,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [26382] {represent,
## perform,
## propos,
## applic,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [26383] {represent,
## perform,
## propos,
## problem,
## applic} => {success} 0.1000000 0.7500000 2.812500 3
## [26384] {represent,
## perform,
## propos,
## problem,
## success} => {applic} 0.1000000 1.0000000 4.285714 3
## [26385] {approach,
## make,
## perform,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [26386] {approach,
## make,
## represent,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [26387] {make,
## represent,
## perform,
## problem,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [26388] {approach,
## make,
## represent,
## perform,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [26389] {approach,
## represent,
## perform,
## problem,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [26390] {approach,
## make,
## represent,
## perform,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [26391] {approach,
## make,
## perform,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [26392] {approach,
## make,
## propos,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [26393] {make,
## perform,
## propos,
## problem,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [26394] {approach,
## make,
## perform,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [26395] {approach,
## perform,
## propos,
## problem,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [26396] {approach,
## make,
## perform,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [26397] {approach,
## make,
## represent,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [26398] {approach,
## make,
## propos,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [26399] {make,
## represent,
## propos,
## problem,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [26400] {approach,
## make,
## represent,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [26401] {approach,
## represent,
## propos,
## problem,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [26402] {approach,
## make,
## represent,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [26403] {make,
## represent,
## perform,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [26404] {make,
## perform,
## propos,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [26405] {make,
## represent,
## propos,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [26406] {make,
## represent,
## perform,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [26407] {represent,
## perform,
## propos,
## problem,
## applic} => {make} 0.1000000 0.7500000 2.500000 3
## [26408] {make,
## represent,
## perform,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [26409] {approach,
## make,
## represent,
## perform,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [26410] {approach,
## make,
## perform,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [26411] {approach,
## make,
## represent,
## propos,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [26412] {make,
## represent,
## perform,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [26413] {approach,
## represent,
## perform,
## propos,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [26414] {approach,
## make,
## represent,
## perform,
## propos} => {applic} 0.1000000 1.0000000 4.285714 3
## [26415] {approach,
## represent,
## perform,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [26416] {approach,
## perform,
## propos,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [26417] {approach,
## represent,
## propos,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [26418] {represent,
## perform,
## propos,
## problem,
## applic} => {approach} 0.1000000 0.7500000 1.875000 3
## [26419] {approach,
## represent,
## perform,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [26420] {approach,
## represent,
## perform,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [26421] {represent,
## perform,
## problem,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [26422] {perform,
## propos,
## problem,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [26423] {represent,
## perform,
## propos,
## problem,
## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [26424] {represent,
## propos,
## problem,
## learn,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [26425] {represent,
## perform,
## propos,
## learn,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [26426] {represent,
## perform,
## propos,
## problem,
## learn} => {applic} 0.1000000 1.0000000 4.285714 3
## [26427] {represent,
## perform,
## propos,
## problem,
## applic} => {featur} 0.1000000 0.7500000 1.406250 3
## [26428] {featur,
## represent,
## perform,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [26429] {featur,
## perform,
## propos,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [26430] {featur,
## represent,
## propos,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [26431] {featur,
## represent,
## perform,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [26432] {featur,
## represent,
## perform,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [26433] {data,
## dataset,
## result,
## work,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [26434] {data,
## represent,
## result,
## work,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26435] {represent,
## dataset,
## result,
## work,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [26436] {data,
## represent,
## dataset,
## result,
## applic} => {work} 0.1000000 1.0000000 2.500000 3
## [26437] {data,
## represent,
## dataset,
## work,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [26438] {data,
## represent,
## dataset,
## result,
## work} => {applic} 0.1000000 1.0000000 4.285714 3
## [26439] {approach,
## data,
## represent,
## task,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [26440] {approach,
## data,
## task,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [26441] {approach,
## represent,
## task,
## propos,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [26442] {data,
## represent,
## task,
## propos,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [26443] {approach,
## data,
## represent,
## propos,
## applic} => {task} 0.1000000 1.0000000 2.727273 3
## [26444] {approach,
## data,
## represent,
## task,
## propos} => {applic} 0.1000000 0.7500000 3.214286 3
## [26445] {approach,
## represent,
## dataset,
## learn,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [26446] {approach,
## dataset,
## propos,
## learn,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [26447] {approach,
## represent,
## dataset,
## propos,
## applic} => {learn} 0.1000000 1.0000000 2.307692 3
## [26448] {approach,
## represent,
## propos,
## learn,
## applic} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26449] {represent,
## dataset,
## propos,
## learn,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [26450] {approach,
## represent,
## dataset,
## propos,
## learn} => {applic} 0.1000000 0.7500000 3.214286 3
## [26451] {approach,
## input,
## show,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [26452] {approach,
## input,
## model,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [26453] {approach,
## input,
## model,
## show,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [26454] {input,
## model,
## show,
## learn,
## recent} => {approach} 0.1000000 1.0000000 2.500000 3
## [26455] {approach,
## input,
## model,
## show,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [26456] {approach,
## model,
## show,
## learn,
## recent} => {input} 0.1000000 1.0000000 4.285714 3
## [26457] {approach,
## input,
## make,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [26458] {approach,
## input,
## make,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [26459] {input,
## make,
## method,
## model,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [26460] {approach,
## input,
## make,
## model,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [26461] {approach,
## input,
## method,
## model,
## show} => {make} 0.1000000 1.0000000 3.333333 3
## [26462] {approach,
## make,
## method,
## model,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [26463] {machin,
## model,
## show,
## learn,
## recent} => {featur} 0.1000000 1.0000000 1.875000 3
## [26464] {featur,
## machin,
## show,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [26465] {featur,
## machin,
## model,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [26466] {featur,
## machin,
## model,
## show,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [26467] {featur,
## model,
## show,
## learn,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [26468] {featur,
## machin,
## model,
## show,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [26469] {show,
## perform,
## problem,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [26470] {model,
## perform,
## problem,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [26471] {model,
## show,
## perform,
## problem,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [26472] {model,
## show,
## problem,
## learn,
## recent} => {perform} 0.1000000 1.0000000 2.142857 3
## [26473] {model,
## show,
## perform,
## learn,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [26474] {model,
## show,
## perform,
## problem,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [26475] {model,
## show,
## algorithm,
## learn,
## recent} => {featur} 0.1000000 1.0000000 1.875000 3
## [26476] {featur,
## show,
## algorithm,
## learn,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [26477] {featur,
## model,
## algorithm,
## learn,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [26478] {featur,
## model,
## show,
## algorithm,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [26479] {featur,
## model,
## show,
## learn,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [26480] {featur,
## model,
## show,
## algorithm,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [26481] {approach,
## classif,
## machin,
## make,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [26482] {classif,
## featur,
## machin,
## make,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [26483] {approach,
## classif,
## featur,
## machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [26484] {approach,
## featur,
## machin,
## make,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [26485] {approach,
## classif,
## featur,
## machin,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [26486] {approach,
## classif,
## featur,
## make,
## method} => {machin} 0.1000000 1.0000000 4.285714 3
## [26487] {classif,
## machin,
## paper,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [26488] {classif,
## featur,
## machin,
## paper,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [26489] {classif,
## featur,
## machin,
## paper,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [26490] {classif,
## featur,
## machin,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [26491] {featur,
## machin,
## paper,
## task,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [26492] {classif,
## featur,
## paper,
## task,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [26493] {data,
## machin,
## model,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [26494] {data,
## featur,
## machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [26495] {data,
## featur,
## machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [26496] {featur,
## machin,
## model,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [26497] {data,
## featur,
## machin,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [26498] {data,
## featur,
## model,
## show,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [26499] {classif,
## architectur,
## dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [26500] {classif,
## architectur,
## experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26501] {architectur,
## dataset,
## experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26502] {classif,
## dataset,
## experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26503] {classif,
## architectur,
## dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26504] {classif,
## architectur,
## dataset,
## experi,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [26505] {classif,
## architectur,
## dataset,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26506] {classif,
## network,
## architectur,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26507] {network,
## architectur,
## dataset,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [26508] {classif,
## network,
## dataset,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26509] {classif,
## network,
## architectur,
## dataset,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [26510] {classif,
## network,
## architectur,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [26511] {classif,
## architectur,
## experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26512] {classif,
## network,
## architectur,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [26513] {network,
## architectur,
## experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26514] {classif,
## network,
## experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26515] {classif,
## network,
## architectur,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26516] {classif,
## network,
## architectur,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [26517] {architectur,
## dataset,
## experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26518] {network,
## architectur,
## dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [26519] {network,
## architectur,
## experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26520] {network,
## dataset,
## experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26521] {network,
## architectur,
## dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26522] {network,
## architectur,
## dataset,
## experi,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [26523] {classif,
## dataset,
## experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26524] {classif,
## network,
## dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [26525] {classif,
## network,
## experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26526] {network,
## dataset,
## experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26527] {classif,
## network,
## dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26528] {classif,
## network,
## dataset,
## experi,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [26529] {classif,
## architectur,
## dataset,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26530] {classif,
## network,
## architectur,
## dataset,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [26531] {classif,
## network,
## architectur,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26532] {network,
## architectur,
## dataset,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26533] {classif,
## network,
## dataset,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26534] {classif,
## network,
## architectur,
## dataset,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [26535] {algorithm,
## architectur,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26536] {architectur,
## improv,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26537] {algorithm,
## architectur,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26538] {algorithm,
## architectur,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26539] {algorithm,
## improv,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26540] {algorithm,
## architectur,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26541] {algorithm,
## architectur,
## improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26542] {architectur,
## dataset,
## improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26543] {algorithm,
## architectur,
## dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26544] {algorithm,
## architectur,
## dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26545] {algorithm,
## dataset,
## improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26546] {algorithm,
## architectur,
## dataset,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [26547] {algorithm,
## architectur,
## improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26548] {network,
## architectur,
## improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26549] {network,
## algorithm,
## architectur,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26550] {network,
## algorithm,
## architectur,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26551] {network,
## algorithm,
## improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26552] {network,
## algorithm,
## architectur,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [26553] {architectur,
## improv,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26554] {architectur,
## dataset,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26555] {architectur,
## dataset,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26556] {architectur,
## dataset,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26557] {dataset,
## improv,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26558] {architectur,
## dataset,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26559] {architectur,
## improv,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26560] {network,
## architectur,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26561] {network,
## architectur,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26562] {network,
## architectur,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26563] {network,
## improv,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26564] {network,
## architectur,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26565] {architectur,
## dataset,
## improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26566] {network,
## architectur,
## improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26567] {network,
## architectur,
## dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26568] {network,
## architectur,
## dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26569] {network,
## dataset,
## improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26570] {network,
## architectur,
## dataset,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [26571] {algorithm,
## architectur,
## improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26572] {algorithm,
## architectur,
## dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26573] {architectur,
## dataset,
## improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26574] {algorithm,
## architectur,
## dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26575] {algorithm,
## dataset,
## improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26576] {algorithm,
## architectur,
## dataset,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26577] {algorithm,
## architectur,
## improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26578] {network,
## algorithm,
## architectur,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26579] {network,
## architectur,
## improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26580] {network,
## algorithm,
## architectur,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26581] {network,
## algorithm,
## improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26582] {network,
## algorithm,
## architectur,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26583] {algorithm,
## architectur,
## dataset,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26584] {network,
## algorithm,
## architectur,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26585] {network,
## architectur,
## dataset,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26586] {network,
## algorithm,
## architectur,
## dataset,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26587] {network,
## algorithm,
## dataset,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26588] {network,
## algorithm,
## architectur,
## dataset,
## improv} => {process} 0.1000000 1.0000000 5.000000 3
## [26589] {architectur,
## dataset,
## improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26590] {network,
## architectur,
## improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26591] {network,
## architectur,
## dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26592] {network,
## architectur,
## dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26593] {network,
## dataset,
## improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26594] {network,
## architectur,
## dataset,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26595] {algorithm,
## architectur,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26596] {algorithm,
## architectur,
## dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26597] {architectur,
## dataset,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26598] {algorithm,
## architectur,
## dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26599] {algorithm,
## dataset,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26600] {algorithm,
## architectur,
## dataset,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26601] {algorithm,
## architectur,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26602] {network,
## algorithm,
## architectur,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26603] {network,
## architectur,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26604] {network,
## algorithm,
## architectur,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26605] {network,
## algorithm,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26606] {network,
## algorithm,
## architectur,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26607] {algorithm,
## architectur,
## dataset,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26608] {network,
## algorithm,
## architectur,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26609] {network,
## architectur,
## dataset,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26610] {network,
## algorithm,
## architectur,
## dataset,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26611] {network,
## algorithm,
## dataset,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26612] {network,
## algorithm,
## architectur,
## dataset,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [26613] {architectur,
## dataset,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26614] {network,
## architectur,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26615] {network,
## architectur,
## dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26616] {network,
## architectur,
## dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26617] {network,
## dataset,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26618] {network,
## architectur,
## dataset,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26619] {algorithm,
## architectur,
## dataset,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26620] {network,
## algorithm,
## architectur,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26621] {network,
## algorithm,
## architectur,
## dataset,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26622] {network,
## architectur,
## dataset,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26623] {network,
## algorithm,
## dataset,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26624] {network,
## algorithm,
## architectur,
## dataset,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26625] {algorithm,
## improv,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26626] {algorithm,
## dataset,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26627] {dataset,
## improv,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26628] {algorithm,
## dataset,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26629] {algorithm,
## dataset,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26630] {algorithm,
## dataset,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26631] {algorithm,
## improv,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26632] {network,
## algorithm,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26633] {network,
## improv,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26634] {network,
## algorithm,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26635] {network,
## algorithm,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26636] {network,
## algorithm,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26637] {algorithm,
## dataset,
## improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26638] {network,
## algorithm,
## improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26639] {network,
## dataset,
## improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26640] {network,
## algorithm,
## dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26641] {network,
## algorithm,
## dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26642] {network,
## algorithm,
## dataset,
## improv,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [26643] {dataset,
## improv,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26644] {network,
## improv,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26645] {network,
## dataset,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26646] {network,
## dataset,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26647] {network,
## dataset,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26648] {network,
## dataset,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26649] {algorithm,
## dataset,
## improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26650] {network,
## algorithm,
## improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26651] {network,
## algorithm,
## dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26652] {network,
## dataset,
## improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26653] {network,
## algorithm,
## dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [26654] {network,
## algorithm,
## dataset,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26655] {approach,
## show,
## algorithm,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26656] {approach,
## network,
## algorithm,
## neural,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [26657] {network,
## show,
## algorithm,
## neural,
## process} => {approach} 0.1000000 1.0000000 2.500000 3
## [26658] {approach,
## network,
## show,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26659] {approach,
## network,
## show,
## algorithm,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26660] {approach,
## network,
## show,
## algorithm,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [26661] {algorithm,
## dataset,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [26662] {network,
## algorithm,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26663] {network,
## algorithm,
## dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [26664] {network,
## dataset,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [26665] {network,
## algorithm,
## dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [26666] {network,
## algorithm,
## dataset,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [26667] {data,
## reduc,
## represent,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [26668] {data,
## featur,
## reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [26669] {data,
## featur,
## reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [26670] {featur,
## reduc,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [26671] {data,
## featur,
## reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [26672] {data,
## featur,
## represent,
## show,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [26673] {data,
## reduc,
## represent,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [26674] {data,
## network,
## reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [26675] {data,
## network,
## reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [26676] {network,
## reduc,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [26677] {data,
## network,
## reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [26678] {data,
## network,
## represent,
## show,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [26679] {data,
## featur,
## reduc,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [26680] {data,
## network,
## reduc,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [26681] {data,
## featur,
## network,
## reduc,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [26682] {featur,
## network,
## reduc,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [26683] {data,
## featur,
## network,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [26684] {data,
## featur,
## network,
## represent,
## task} => {reduc} 0.1000000 1.0000000 4.285714 3
## [26685] {data,
## featur,
## reduc,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [26686] {data,
## network,
## reduc,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [26687] {data,
## featur,
## network,
## reduc,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [26688] {featur,
## network,
## reduc,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [26689] {data,
## featur,
## network,
## reduc,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [26690] {data,
## featur,
## network,
## show,
## task} => {reduc} 0.1000000 1.0000000 4.285714 3
## [26691] {featur,
## reduc,
## represent,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [26692] {network,
## reduc,
## represent,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [26693] {featur,
## network,
## reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [26694] {featur,
## network,
## reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [26695] {featur,
## network,
## reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [26696] {featur,
## network,
## represent,
## show,
## task} => {reduc} 0.1000000 1.0000000 4.285714 3
## [26697] {data,
## featur,
## reduc,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [26698] {data,
## network,
## reduc,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [26699] {data,
## featur,
## network,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [26700] {data,
## featur,
## network,
## reduc,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [26701] {featur,
## network,
## reduc,
## represent,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [26702] {data,
## featur,
## network,
## represent,
## show} => {reduc} 0.1000000 1.0000000 4.285714 3
## [26703] {paper,
## represent,
## train,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [26704] {featur,
## paper,
## train,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [26705] {featur,
## paper,
## represent,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [26706] {featur,
## paper,
## represent,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [26707] {featur,
## represent,
## train,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [26708] {featur,
## paper,
## represent,
## train,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26709] {achiev,
## dataset,
## improv,
## neural,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [26710] {model,
## achiev,
## dataset,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26711] {model,
## achiev,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26712] {model,
## achiev,
## dataset,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26713] {model,
## achiev,
## dataset,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [26714] {model,
## dataset,
## improv,
## neural,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26715] {approach,
## achiev,
## neural,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [26716] {approach,
## network,
## achiev,
## neural,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [26717] {network,
## achiev,
## neural,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [26718] {approach,
## network,
## achiev,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [26719] {approach,
## network,
## achiev,
## neural,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [26720] {approach,
## network,
## neural,
## propos,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [26721] {approach,
## featur,
## method,
## achiev,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26722] {approach,
## method,
## network,
## achiev,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [26723] {approach,
## featur,
## method,
## network,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [26724] {featur,
## method,
## network,
## achiev,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26725] {approach,
## featur,
## network,
## achiev,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26726] {approach,
## featur,
## method,
## network,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26727] {featur,
## represent,
## task,
## achiev,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [26728] {network,
## represent,
## task,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [26729] {featur,
## network,
## task,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [26730] {featur,
## network,
## represent,
## task,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [26731] {featur,
## network,
## represent,
## achiev,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [26732] {featur,
## network,
## represent,
## task,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26733] {approach,
## model,
## achiev,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26734] {approach,
## network,
## achiev,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [26735] {approach,
## model,
## network,
## achiev,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [26736] {approach,
## model,
## network,
## achiev,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26737] {model,
## network,
## achiev,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26738] {approach,
## model,
## network,
## dataset,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [26739] {data,
## represent,
## achiev,
## dataset,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [26740] {data,
## featur,
## achiev,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [26741] {data,
## featur,
## represent,
## achiev,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [26742] {data,
## featur,
## represent,
## achiev,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26743] {featur,
## represent,
## achiev,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [26744] {data,
## featur,
## represent,
## dataset,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [26745] {data,
## task,
## object,
## propos,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [26746] {data,
## featur,
## task,
## object,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [26747] {featur,
## task,
## object,
## propos,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [26748] {data,
## featur,
## object,
## propos,
## optim} => {task} 0.1000000 1.0000000 2.727273 3
## [26749] {data,
## featur,
## task,
## propos,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [26750] {data,
## featur,
## task,
## object,
## propos} => {optim} 0.1000000 0.7500000 3.214286 3
## [26751] {architectur,
## improv,
## perform,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [26752] {network,
## architectur,
## improv,
## optim,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [26753] {network,
## architectur,
## improv,
## perform,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [26754] {network,
## architectur,
## perform,
## optim,
## work} => {improv} 0.1000000 1.0000000 3.333333 3
## [26755] {network,
## improv,
## perform,
## optim,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26756] {network,
## architectur,
## improv,
## perform,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [26757] {paper,
## task,
## train,
## learn,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [26758] {featur,
## paper,
## task,
## train,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [26759] {featur,
## paper,
## task,
## learn,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [26760] {featur,
## paper,
## train,
## learn,
## signific} => {task} 0.1000000 1.0000000 2.727273 3
## [26761] {featur,
## task,
## train,
## learn,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [26762] {featur,
## paper,
## task,
## train,
## learn} => {signific} 0.1000000 1.0000000 3.750000 3
## [26763] {featur,
## represent,
## train,
## result,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [26764] {network,
## represent,
## train,
## result,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [26765] {featur,
## network,
## train,
## result,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [26766] {featur,
## network,
## represent,
## result,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [26767] {featur,
## network,
## represent,
## train,
## signific} => {result} 0.1000000 1.0000000 3.000000 3
## [26768] {featur,
## network,
## represent,
## train,
## result} => {signific} 0.1000000 1.0000000 3.750000 3
## [26769] {approach,
## method,
## represent,
## learn,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [26770] {approach,
## method,
## propos,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [26771] {approach,
## method,
## represent,
## propos,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [26772] {method,
## represent,
## propos,
## learn,
## success} => {approach} 0.1000000 0.7500000 1.875000 3
## [26773] {approach,
## represent,
## propos,
## learn,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [26774] {approach,
## method,
## represent,
## propos,
## learn} => {success} 0.1000000 1.0000000 3.750000 3
## [26775] {method,
## represent,
## show,
## learn,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [26776] {method,
## represent,
## propos,
## learn,
## success} => {show} 0.1000000 0.7500000 1.406250 3
## [26777] {method,
## show,
## propos,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [26778] {method,
## represent,
## show,
## propos,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [26779] {represent,
## show,
## propos,
## learn,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [26780] {method,
## represent,
## show,
## propos,
## learn} => {success} 0.1000000 1.0000000 3.750000 3
## [26781] {method,
## represent,
## propos,
## learn,
## success} => {featur} 0.1000000 0.7500000 1.406250 3
## [26782] {featur,
## method,
## represent,
## learn,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [26783] {featur,
## method,
## propos,
## learn,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [26784] {featur,
## method,
## represent,
## propos,
## success} => {learn} 0.1000000 1.0000000 2.307692 3
## [26785] {featur,
## represent,
## propos,
## learn,
## success} => {method} 0.1000000 1.0000000 2.727273 3
## [26786] {featur,
## method,
## represent,
## propos,
## learn} => {success} 0.1000000 1.0000000 3.750000 3
## [26787] {approach,
## represent,
## task,
## propos,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [26788] {approach,
## featur,
## represent,
## task,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [26789] {approach,
## featur,
## task,
## propos,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [26790] {featur,
## represent,
## task,
## propos,
## success} => {approach} 0.1000000 1.0000000 2.500000 3
## [26791] {approach,
## featur,
## represent,
## propos,
## success} => {task} 0.1000000 1.0000000 2.727273 3
## [26792] {approach,
## featur,
## represent,
## task,
## propos} => {success} 0.1000000 0.7500000 2.812500 3
## [26793] {classif,
## method,
## architectur,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [26794] {approach,
## classif,
## architectur,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [26795] {approach,
## classif,
## method,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [26796] {approach,
## method,
## architectur,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [26797] {approach,
## classif,
## method,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26798] {approach,
## classif,
## method,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [26799] {classif,
## method,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26800] {classif,
## architectur,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26801] {classif,
## method,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26802] {method,
## architectur,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26803] {classif,
## method,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26804] {classif,
## method,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26805] {classif,
## method,
## architectur,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [26806] {classif,
## network,
## architectur,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [26807] {classif,
## method,
## network,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [26808] {method,
## network,
## architectur,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [26809] {classif,
## method,
## network,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26810] {classif,
## method,
## network,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [26811] {approach,
## classif,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26812] {classif,
## architectur,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26813] {approach,
## classif,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26814] {approach,
## architectur,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26815] {approach,
## classif,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26816] {approach,
## classif,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26817] {approach,
## classif,
## architectur,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [26818] {classif,
## network,
## architectur,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [26819] {approach,
## classif,
## network,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [26820] {approach,
## network,
## architectur,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [26821] {approach,
## classif,
## network,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26822] {approach,
## classif,
## network,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [26823] {classif,
## architectur,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26824] {classif,
## network,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26825] {classif,
## network,
## architectur,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [26826] {network,
## architectur,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26827] {classif,
## network,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26828] {classif,
## network,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26829] {approach,
## classif,
## method,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [26830] {classif,
## method,
## architectur,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26831] {approach,
## classif,
## architectur,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26832] {approach,
## method,
## architectur,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26833] {approach,
## classif,
## method,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26834] {approach,
## classif,
## method,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26835] {approach,
## classif,
## method,
## architectur,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [26836] {classif,
## method,
## network,
## architectur,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [26837] {approach,
## classif,
## network,
## architectur,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [26838] {approach,
## method,
## network,
## architectur,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [26839] {approach,
## classif,
## method,
## network,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26840] {approach,
## classif,
## method,
## network,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [26841] {classif,
## method,
## architectur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26842] {classif,
## method,
## network,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [26843] {classif,
## network,
## architectur,
## experi,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [26844] {method,
## network,
## architectur,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26845] {classif,
## method,
## network,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26846] {classif,
## method,
## network,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26847] {approach,
## classif,
## architectur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26848] {approach,
## classif,
## network,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [26849] {classif,
## network,
## architectur,
## experi,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [26850] {approach,
## network,
## architectur,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26851] {approach,
## classif,
## network,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26852] {approach,
## classif,
## network,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26853] {classif,
## architectur,
## dataset,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26854] {classif,
## network,
## architectur,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [26855] {classif,
## network,
## architectur,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [26856] {network,
## architectur,
## dataset,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26857] {classif,
## network,
## dataset,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26858] {classif,
## network,
## architectur,
## dataset,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26859] {classif,
## featur,
## architectur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26860] {classif,
## network,
## architectur,
## experi,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [26861] {classif,
## featur,
## network,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [26862] {featur,
## network,
## architectur,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26863] {classif,
## featur,
## network,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26864] {classif,
## featur,
## network,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26865] {approach,
## method,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26866] {method,
## architectur,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26867] {approach,
## architectur,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26868] {approach,
## method,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26869] {approach,
## method,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26870] {approach,
## method,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26871] {approach,
## method,
## architectur,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [26872] {method,
## network,
## architectur,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [26873] {approach,
## network,
## architectur,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [26874] {approach,
## method,
## network,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [26875] {approach,
## method,
## network,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26876] {approach,
## method,
## network,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [26877] {method,
## architectur,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26878] {method,
## network,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26879] {network,
## architectur,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26880] {method,
## network,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26881] {method,
## network,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26882] {method,
## network,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26883] {approach,
## architectur,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26884] {approach,
## network,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26885] {network,
## architectur,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26886] {approach,
## network,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26887] {approach,
## network,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26888] {approach,
## network,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26889] {approach,
## method,
## architectur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26890] {approach,
## method,
## network,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [26891] {method,
## network,
## architectur,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26892] {approach,
## network,
## architectur,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26893] {approach,
## method,
## network,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [26894] {approach,
## method,
## network,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26895] {approach,
## classif,
## method,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26896] {classif,
## method,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26897] {approach,
## classif,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26898] {approach,
## classif,
## method,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26899] {approach,
## method,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26900] {approach,
## classif,
## method,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26901] {approach,
## classif,
## method,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [26902] {classif,
## method,
## network,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [26903] {approach,
## classif,
## network,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [26904] {approach,
## classif,
## method,
## network,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [26905] {approach,
## method,
## network,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [26906] {approach,
## classif,
## method,
## network,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [26907] {classif,
## method,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26908] {classif,
## method,
## network,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26909] {classif,
## network,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26910] {classif,
## method,
## network,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26911] {method,
## network,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26912] {classif,
## method,
## network,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26913] {approach,
## classif,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26914] {approach,
## classif,
## network,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26915] {classif,
## network,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26916] {approach,
## classif,
## network,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26917] {approach,
## network,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26918] {approach,
## classif,
## network,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26919] {approach,
## classif,
## method,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26920] {approach,
## classif,
## method,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [26921] {classif,
## method,
## network,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26922] {approach,
## classif,
## network,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26923] {approach,
## method,
## network,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26924] {approach,
## classif,
## method,
## network,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26925] {approach,
## method,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [26926] {approach,
## method,
## network,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [26927] {method,
## network,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26928] {approach,
## network,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26929] {approach,
## method,
## network,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [26930] {approach,
## method,
## network,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26931] {approach,
## method,
## dataset,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [26932] {approach,
## method,
## show,
## experi,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26933] {approach,
## method,
## show,
## dataset,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [26934] {method,
## show,
## dataset,
## experi,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [26935] {approach,
## show,
## dataset,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [26936] {approach,
## method,
## show,
## dataset,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [26937] {approach,
## method,
## dataset,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [26938] {approach,
## method,
## experi,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26939] {approach,
## method,
## dataset,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [26940] {method,
## dataset,
## experi,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26941] {approach,
## dataset,
## experi,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26942] {approach,
## method,
## dataset,
## perform,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26943] {approach,
## method,
## show,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [26944] {approach,
## method,
## experi,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [26945] {approach,
## method,
## show,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [26946] {method,
## show,
## experi,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26947] {approach,
## show,
## experi,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26948] {approach,
## method,
## show,
## perform,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26949] {approach,
## method,
## show,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [26950] {approach,
## method,
## dataset,
## experi,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [26951] {approach,
## method,
## show,
## experi,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26952] {method,
## show,
## dataset,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26953] {approach,
## show,
## dataset,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26954] {approach,
## method,
## show,
## dataset,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [26955] {method,
## show,
## dataset,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [26956] {method,
## dataset,
## experi,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [26957] {method,
## show,
## experi,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26958] {method,
## show,
## dataset,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [26959] {show,
## dataset,
## experi,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [26960] {method,
## show,
## dataset,
## perform,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26961] {approach,
## show,
## dataset,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [26962] {approach,
## dataset,
## experi,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [26963] {approach,
## show,
## experi,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [26964] {approach,
## show,
## dataset,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [26965] {show,
## dataset,
## experi,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [26966] {approach,
## show,
## dataset,
## perform,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [26967] {classif,
## show,
## object,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [26968] {classif,
## featur,
## show,
## object,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [26969] {classif,
## featur,
## object,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [26970] {classif,
## featur,
## show,
## object,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [26971] {featur,
## show,
## object,
## perform,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [26972] {classif,
## featur,
## show,
## perform,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [26973] {data,
## task,
## object,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [26974] {data,
## model,
## task,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [26975] {data,
## model,
## task,
## object,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [26976] {model,
## task,
## object,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [26977] {data,
## model,
## object,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [26978] {data,
## model,
## task,
## propos,
## learn} => {object} 0.1000000 0.7500000 2.812500 3
## [26979] {data,
## task,
## object,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [26980] {data,
## featur,
## task,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [26981] {data,
## featur,
## task,
## object,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [26982] {featur,
## task,
## object,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [26983] {data,
## featur,
## object,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [26984] {data,
## featur,
## task,
## propos,
## learn} => {object} 0.1000000 0.7500000 2.812500 3
## [26985] {data,
## model,
## task,
## object,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [26986] {data,
## featur,
## task,
## object,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [26987] {data,
## featur,
## model,
## task,
## object} => {learn} 0.1000000 1.0000000 2.307692 3
## [26988] {featur,
## model,
## task,
## object,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [26989] {data,
## featur,
## model,
## object,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [26990] {data,
## model,
## task,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [26991] {data,
## featur,
## task,
## object,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [26992] {data,
## featur,
## model,
## task,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [26993] {featur,
## model,
## task,
## object,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [26994] {data,
## featur,
## model,
## object,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [26995] {data,
## featur,
## model,
## task,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [26996] {model,
## task,
## object,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [26997] {featur,
## task,
## object,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [26998] {featur,
## model,
## task,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [26999] {featur,
## model,
## task,
## object,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27000] {featur,
## model,
## object,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27001] {featur,
## model,
## task,
## propos,
## learn} => {object} 0.1000000 0.7500000 2.812500 3
## [27002] {data,
## model,
## object,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27003] {data,
## featur,
## object,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27004] {data,
## featur,
## model,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27005] {data,
## featur,
## model,
## object,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27006] {featur,
## model,
## object,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27007] {featur,
## model,
## dataset,
## object,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27008] {model,
## network,
## dataset,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [27009] {featur,
## network,
## dataset,
## object,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [27010] {featur,
## model,
## network,
## dataset,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [27011] {featur,
## model,
## network,
## object,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27012] {featur,
## model,
## network,
## dataset,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [27013] {approach,
## classif,
## method,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [27014] {classif,
## method,
## architectur,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27015] {approach,
## classif,
## architectur,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [27016] {approach,
## classif,
## method,
## architectur,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [27017] {approach,
## method,
## architectur,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [27018] {approach,
## classif,
## method,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27019] {approach,
## classif,
## method,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [27020] {classif,
## method,
## network,
## architectur,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [27021] {approach,
## classif,
## network,
## architectur,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [27022] {approach,
## classif,
## method,
## network,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [27023] {approach,
## method,
## network,
## architectur,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [27024] {approach,
## classif,
## method,
## network,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27025] {classif,
## method,
## architectur,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27026] {classif,
## method,
## network,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [27027] {classif,
## network,
## architectur,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [27028] {classif,
## method,
## network,
## architectur,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [27029] {method,
## network,
## architectur,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [27030] {classif,
## method,
## network,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27031] {approach,
## classif,
## architectur,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27032] {approach,
## classif,
## network,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [27033] {classif,
## network,
## architectur,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27034] {approach,
## classif,
## network,
## architectur,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [27035] {approach,
## network,
## architectur,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [27036] {approach,
## classif,
## network,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27037] {approach,
## classif,
## method,
## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27038] {approach,
## classif,
## method,
## network,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [27039] {classif,
## method,
## network,
## architectur,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27040] {approach,
## classif,
## network,
## architectur,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [27041] {approach,
## method,
## network,
## architectur,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [27042] {approach,
## classif,
## method,
## network,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27043] {algorithm,
## architectur,
## improv,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27044] {algorithm,
## architectur,
## dataset,
## improv,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [27045] {architectur,
## dataset,
## improv,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27046] {algorithm,
## architectur,
## dataset,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [27047] {algorithm,
## architectur,
## dataset,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [27048] {algorithm,
## dataset,
## improv,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27049] {algorithm,
## architectur,
## improv,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [27050] {network,
## algorithm,
## architectur,
## improv,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [27051] {network,
## architectur,
## improv,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27052] {network,
## algorithm,
## architectur,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [27053] {network,
## algorithm,
## architectur,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [27054] {network,
## algorithm,
## improv,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27055] {algorithm,
## architectur,
## dataset,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [27056] {network,
## algorithm,
## architectur,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27057] {network,
## architectur,
## dataset,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27058] {network,
## algorithm,
## architectur,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [27059] {network,
## algorithm,
## architectur,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [27060] {network,
## algorithm,
## dataset,
## improv,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [27061] {architectur,
## dataset,
## improv,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [27062] {network,
## architectur,
## improv,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27063] {network,
## architectur,
## dataset,
## improv,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [27064] {network,
## architectur,
## dataset,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [27065] {network,
## architectur,
## dataset,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [27066] {network,
## dataset,
## improv,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27067] {algorithm,
## architectur,
## dataset,
## improv,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [27068] {network,
## algorithm,
## architectur,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27069] {network,
## algorithm,
## architectur,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [27070] {network,
## architectur,
## dataset,
## improv,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27071] {network,
## algorithm,
## architectur,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [27072] {network,
## algorithm,
## dataset,
## improv,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27073] {approach,
## method,
## architectur,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27074] {approach,
## method,
## network,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [27075] {method,
## network,
## architectur,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27076] {approach,
## network,
## architectur,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [27077] {approach,
## method,
## network,
## architectur,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [27078] {approach,
## method,
## network,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27079] {algorithm,
## architectur,
## dataset,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [27080] {network,
## algorithm,
## architectur,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27081] {network,
## algorithm,
## architectur,
## dataset,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [27082] {network,
## architectur,
## dataset,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27083] {network,
## algorithm,
## architectur,
## dataset,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [27084] {network,
## algorithm,
## dataset,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27085] {method,
## architectur,
## dataset,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27086] {method,
## network,
## architectur,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [27087] {method,
## network,
## architectur,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27088] {method,
## network,
## architectur,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [27089] {network,
## architectur,
## dataset,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [27090] {method,
## network,
## dataset,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [27091] {approach,
## make,
## dataset,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [27092] {approach,
## make,
## perform,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27093] {approach,
## make,
## dataset,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [27094] {make,
## dataset,
## perform,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27095] {approach,
## make,
## dataset,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [27096] {approach,
## dataset,
## perform,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [27097] {approach,
## make,
## dataset,
## perform,
## problem} => {model} 0.1000000 1.0000000 1.875000 3
## [27098] {approach,
## make,
## model,
## perform,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27099] {approach,
## make,
## model,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [27100] {make,
## model,
## dataset,
## perform,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [27101] {approach,
## make,
## model,
## dataset,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [27102] {approach,
## model,
## dataset,
## perform,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [27103] {approach,
## make,
## perform,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27104] {approach,
## make,
## model,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [27105] {approach,
## make,
## model,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [27106] {make,
## model,
## perform,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27107] {approach,
## make,
## model,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [27108] {approach,
## model,
## perform,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [27109] {approach,
## make,
## represent,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [27110] {approach,
## make,
## perform,
## propos,
## problem} => {represent} 0.1000000 1.0000000 2.000000 3
## [27111] {approach,
## make,
## represent,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [27112] {make,
## represent,
## perform,
## propos,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [27113] {approach,
## make,
## represent,
## perform,
## propos} => {problem} 0.1000000 1.0000000 3.333333 3
## [27114] {approach,
## represent,
## perform,
## propos,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [27115] {approach,
## make,
## dataset,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27116] {approach,
## make,
## model,
## dataset,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [27117] {approach,
## make,
## model,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27118] {make,
## model,
## dataset,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27119] {approach,
## make,
## model,
## dataset,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [27120] {approach,
## model,
## dataset,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [27121] {make,
## dataset,
## perform,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27122] {make,
## model,
## dataset,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [27123] {make,
## model,
## perform,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27124] {make,
## model,
## dataset,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [27125] {make,
## model,
## dataset,
## perform,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [27126] {model,
## dataset,
## perform,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [27127] {approach,
## data,
## make,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [27128] {approach,
## data,
## featur,
## make,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [27129] {approach,
## featur,
## make,
## represent,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [27130] {data,
## featur,
## make,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [27131] {approach,
## data,
## featur,
## make,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [27132] {approach,
## data,
## featur,
## represent,
## task} => {make} 0.1000000 0.7500000 2.500000 3
## [27133] {approach,
## make,
## represent,
## task,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [27134] {approach,
## featur,
## make,
## represent,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [27135] {approach,
## featur,
## make,
## task,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [27136] {featur,
## make,
## represent,
## task,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27137] {approach,
## featur,
## make,
## represent,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [27138] {approach,
## featur,
## represent,
## task,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [27139] {approach,
## make,
## dataset,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27140] {approach,
## make,
## model,
## dataset,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [27141] {approach,
## make,
## model,
## perform,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27142] {approach,
## make,
## model,
## dataset,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [27143] {make,
## model,
## dataset,
## perform,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [27144] {approach,
## model,
## dataset,
## perform,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [27145] {data,
## make,
## represent,
## perform,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [27146] {data,
## featur,
## make,
## represent,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [27147] {data,
## featur,
## make,
## perform,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [27148] {featur,
## make,
## represent,
## perform,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [27149] {data,
## featur,
## make,
## represent,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [27150] {data,
## featur,
## represent,
## perform,
## propos} => {make} 0.1000000 1.0000000 3.333333 3
## [27151] {make,
## represent,
## dataset,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27152] {make,
## dataset,
## perform,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27153] {make,
## represent,
## dataset,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27154] {make,
## represent,
## perform,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27155] {make,
## represent,
## dataset,
## propos,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [27156] {represent,
## dataset,
## perform,
## propos,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [27157] {make,
## represent,
## dataset,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27158] {make,
## model,
## dataset,
## perform,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [27159] {make,
## model,
## represent,
## dataset,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [27160] {make,
## model,
## represent,
## perform,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27161] {make,
## model,
## represent,
## dataset,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [27162] {model,
## represent,
## dataset,
## perform,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [27163] {make,
## dataset,
## perform,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27164] {make,
## model,
## dataset,
## perform,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [27165] {make,
## model,
## dataset,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27166] {make,
## model,
## perform,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27167] {make,
## model,
## dataset,
## propos,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [27168] {model,
## dataset,
## perform,
## propos,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [27169] {make,
## model,
## dataset,
## perform,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [27170] {featur,
## make,
## dataset,
## perform,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27171] {featur,
## make,
## model,
## dataset,
## perform} => {learn} 0.1000000 1.0000000 2.307692 3
## [27172] {featur,
## make,
## model,
## perform,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27173] {featur,
## make,
## model,
## dataset,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [27174] {featur,
## model,
## dataset,
## perform,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [27175] {make,
## represent,
## dataset,
## perform,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [27176] {make,
## model,
## represent,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [27177] {make,
## model,
## dataset,
## perform,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [27178] {make,
## model,
## represent,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27179] {make,
## model,
## represent,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [27180] {model,
## represent,
## dataset,
## perform,
## propos} => {make} 0.1000000 1.0000000 3.333333 3
## [27181] {make,
## represent,
## perform,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27182] {make,
## model,
## represent,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27183] {make,
## model,
## perform,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27184] {make,
## model,
## represent,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27185] {make,
## model,
## represent,
## propos,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [27186] {model,
## represent,
## perform,
## propos,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [27187] {make,
## represent,
## dataset,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27188] {make,
## model,
## represent,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27189] {make,
## model,
## dataset,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27190] {make,
## model,
## represent,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27191] {make,
## model,
## represent,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27192] {model,
## represent,
## dataset,
## propos,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [27193] {classif,
## show,
## perform,
## problem,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27194] {classif,
## featur,
## perform,
## problem,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [27195] {classif,
## featur,
## show,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [27196] {classif,
## featur,
## show,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [27197] {classif,
## featur,
## show,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [27198] {featur,
## show,
## perform,
## problem,
## learn} => {classif} 0.1000000 1.0000000 3.750000 3
## [27199] {approach,
## classif,
## method,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27200] {approach,
## classif,
## method,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [27201] {classif,
## method,
## dataset,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [27202] {approach,
## classif,
## dataset,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [27203] {approach,
## classif,
## method,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [27204] {approach,
## method,
## dataset,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [27205] {approach,
## classif,
## method,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [27206] {approach,
## classif,
## method,
## show,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [27207] {classif,
## method,
## show,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [27208] {approach,
## classif,
## show,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [27209] {approach,
## classif,
## method,
## show,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [27210] {approach,
## method,
## show,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [27211] {approach,
## classif,
## method,
## dataset,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [27212] {approach,
## classif,
## method,
## show,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27213] {classif,
## method,
## show,
## dataset,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [27214] {approach,
## classif,
## show,
## dataset,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [27215] {approach,
## classif,
## method,
## show,
## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [27216] {approach,
## method,
## show,
## dataset,
## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [27217] {classif,
## method,
## dataset,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [27218] {classif,
## method,
## show,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27219] {classif,
## method,
## show,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [27220] {classif,
## show,
## dataset,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [27221] {classif,
## method,
## show,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [27222] {method,
## show,
## dataset,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [27223] {approach,
## classif,
## dataset,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [27224] {approach,
## classif,
## show,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27225] {approach,
## classif,
## show,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [27226] {classif,
## show,
## dataset,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [27227] {approach,
## classif,
## show,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [27228] {approach,
## show,
## dataset,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [27229] {approach,
## classif,
## method,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27230] {approach,
## classif,
## method,
## network,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [27231] {classif,
## method,
## network,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27232] {approach,
## classif,
## network,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [27233] {approach,
## classif,
## method,
## network,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [27234] {approach,
## method,
## network,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [27235] {approach,
## classif,
## method,
## dataset,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [27236] {approach,
## classif,
## method,
## show,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27237] {approach,
## classif,
## method,
## show,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [27238] {classif,
## method,
## show,
## dataset,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [27239] {approach,
## classif,
## show,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [27240] {approach,
## method,
## show,
## dataset,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [27241] {approach,
## classif,
## method,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [27242] {approach,
## classif,
## featur,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [27243] {approach,
## classif,
## featur,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [27244] {classif,
## featur,
## method,
## model,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [27245] {approach,
## classif,
## featur,
## model,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [27246] {approach,
## featur,
## method,
## model,
## show} => {classif} 0.1000000 0.7500000 2.812500 3
## [27247] {approach,
## method,
## dataset,
## perform,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [27248] {approach,
## method,
## show,
## perform,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27249] {approach,
## method,
## show,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [27250] {method,
## show,
## dataset,
## perform,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [27251] {approach,
## show,
## dataset,
## perform,
## problem} => {method} 0.1000000 1.0000000 2.727273 3
## [27252] {approach,
## method,
## show,
## dataset,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [27253] {data,
## task,
## perform,
## propos,
## problem} => {featur} 0.1000000 1.0000000 1.875000 3
## [27254] {data,
## featur,
## task,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [27255] {featur,
## task,
## perform,
## propos,
## problem} => {data} 0.1000000 1.0000000 2.307692 3
## [27256] {data,
## featur,
## task,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [27257] {data,
## featur,
## perform,
## propos,
## problem} => {task} 0.1000000 1.0000000 2.727273 3
## [27258] {data,
## featur,
## task,
## perform,
## propos} => {problem} 0.1000000 1.0000000 3.333333 3
## [27259] {approach,
## dataset,
## perform,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27260] {approach,
## model,
## dataset,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [27261] {approach,
## model,
## perform,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27262] {approach,
## model,
## dataset,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [27263] {model,
## dataset,
## perform,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27264] {approach,
## model,
## dataset,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [27265] {data,
## paper,
## show,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [27266] {data,
## featur,
## paper,
## task,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [27267] {featur,
## paper,
## show,
## task,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [27268] {data,
## featur,
## paper,
## show,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [27269] {data,
## featur,
## paper,
## show,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [27270] {data,
## featur,
## show,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [27271] {featur,
## paper,
## represent,
## task,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [27272] {network,
## paper,
## represent,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [27273] {featur,
## network,
## paper,
## task,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [27274] {featur,
## network,
## paper,
## represent,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [27275] {featur,
## network,
## paper,
## represent,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [27276] {featur,
## network,
## represent,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [27277] {data,
## task,
## recognit,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27278] {data,
## represent,
## task,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [27279] {represent,
## task,
## recognit,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27280] {data,
## represent,
## recognit,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27281] {data,
## represent,
## task,
## recognit,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [27282] {data,
## represent,
## task,
## result,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [27283] {data,
## task,
## recognit,
## result,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [27284] {data,
## show,
## task,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [27285] {show,
## task,
## recognit,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27286] {data,
## show,
## recognit,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27287] {data,
## show,
## task,
## recognit,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [27288] {data,
## show,
## task,
## result,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [27289] {data,
## represent,
## task,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [27290] {data,
## show,
## task,
## recognit,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [27291] {represent,
## show,
## task,
## recognit,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [27292] {data,
## represent,
## show,
## recognit,
## result} => {task} 0.1000000 1.0000000 2.727273 3
## [27293] {data,
## represent,
## show,
## task,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [27294] {data,
## represent,
## show,
## task,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [27295] {represent,
## task,
## recognit,
## result,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [27296] {show,
## task,
## recognit,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27297] {represent,
## show,
## task,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [27298] {represent,
## show,
## recognit,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27299] {represent,
## show,
## task,
## recognit,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [27300] {represent,
## show,
## task,
## result,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [27301] {data,
## represent,
## recognit,
## result,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [27302] {data,
## show,
## recognit,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27303] {data,
## represent,
## show,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [27304] {represent,
## show,
## recognit,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27305] {data,
## represent,
## show,
## recognit,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [27306] {data,
## represent,
## show,
## result,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [27307] {data,
## task,
## dataset,
## recognit,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [27308] {data,
## network,
## task,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [27309] {data,
## network,
## task,
## recognit,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27310] {network,
## task,
## dataset,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27311] {data,
## network,
## dataset,
## recognit,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27312] {data,
## network,
## task,
## dataset,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [27313] {data,
## represent,
## task,
## recognit,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [27314] {data,
## show,
## task,
## recognit,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27315] {data,
## represent,
## show,
## task,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [27316] {represent,
## show,
## task,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27317] {data,
## represent,
## show,
## recognit,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27318] {data,
## represent,
## show,
## task,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [27319] {data,
## train,
## dataset,
## recognit,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27320] {data,
## represent,
## train,
## dataset,
## recognit} => {learn} 0.1000000 1.0000000 2.307692 3
## [27321] {data,
## represent,
## train,
## recognit,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27322] {represent,
## train,
## dataset,
## recognit,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27323] {data,
## represent,
## dataset,
## recognit,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [27324] {data,
## represent,
## train,
## dataset,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [27325] {algorithm,
## dataset,
## improv,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [27326] {network,
## algorithm,
## improv,
## neural,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27327] {network,
## dataset,
## improv,
## neural,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27328] {network,
## algorithm,
## dataset,
## improv,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [27329] {network,
## algorithm,
## dataset,
## improv,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [27330] {network,
## algorithm,
## dataset,
## neural,
## result} => {improv} 0.1000000 1.0000000 3.333333 3
## [27331] {train,
## algorithm,
## dataset,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [27332] {network,
## train,
## algorithm,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27333] {network,
## algorithm,
## dataset,
## improv,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [27334] {network,
## train,
## dataset,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27335] {network,
## train,
## algorithm,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [27336] {network,
## train,
## algorithm,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [27337] {approach,
## algorithm,
## dataset,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [27338] {approach,
## algorithm,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27339] {algorithm,
## dataset,
## improv,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27340] {approach,
## dataset,
## improv,
## neural,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27341] {approach,
## algorithm,
## dataset,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [27342] {approach,
## algorithm,
## dataset,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [27343] {approach,
## algorithm,
## dataset,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [27344] {approach,
## network,
## algorithm,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27345] {network,
## algorithm,
## dataset,
## improv,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [27346] {approach,
## network,
## dataset,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27347] {approach,
## network,
## algorithm,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [27348] {approach,
## network,
## algorithm,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [27349] {approach,
## algorithm,
## improv,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27350] {approach,
## network,
## algorithm,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [27351] {network,
## algorithm,
## improv,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27352] {approach,
## network,
## improv,
## neural,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27353] {approach,
## network,
## algorithm,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [27354] {approach,
## network,
## algorithm,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [27355] {algorithm,
## dataset,
## improv,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [27356] {network,
## algorithm,
## improv,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27357] {network,
## algorithm,
## dataset,
## improv,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [27358] {network,
## dataset,
## improv,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27359] {network,
## algorithm,
## dataset,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [27360] {network,
## algorithm,
## dataset,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [27361] {algorithm,
## dataset,
## improv,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27362] {network,
## algorithm,
## dataset,
## improv,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [27363] {network,
## algorithm,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27364] {network,
## dataset,
## improv,
## neural,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27365] {network,
## algorithm,
## dataset,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [27366] {network,
## algorithm,
## dataset,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [27367] {approach,
## dataset,
## improv,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27368] {approach,
## network,
## dataset,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [27369] {approach,
## network,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27370] {network,
## dataset,
## improv,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27371] {approach,
## network,
## dataset,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [27372] {approach,
## network,
## dataset,
## neural,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [27373] {approach,
## method,
## dataset,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [27374] {approach,
## method,
## show,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27375] {approach,
## method,
## show,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [27376] {method,
## show,
## dataset,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [27377] {approach,
## show,
## dataset,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [27378] {approach,
## method,
## show,
## dataset,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [27379] {approach,
## algorithm,
## dataset,
## improv,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27380] {approach,
## network,
## algorithm,
## dataset,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [27381] {approach,
## network,
## algorithm,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27382] {network,
## algorithm,
## dataset,
## improv,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27383] {approach,
## network,
## dataset,
## improv,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [27384] {approach,
## network,
## algorithm,
## dataset,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [27385] {approach,
## train,
## neural,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [27386] {approach,
## network,
## train,
## neural,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [27387] {network,
## train,
## neural,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [27388] {approach,
## network,
## neural,
## propos,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [27389] {approach,
## network,
## train,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [27390] {approach,
## network,
## train,
## neural,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [27391] {approach,
## dataset,
## neural,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [27392] {approach,
## network,
## dataset,
## neural,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [27393] {approach,
## network,
## neural,
## propos,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27394] {network,
## dataset,
## neural,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [27395] {approach,
## network,
## dataset,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [27396] {approach,
## network,
## dataset,
## neural,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [27397] {represent,
## task,
## train,
## result,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [27398] {network,
## task,
## train,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27399] {network,
## represent,
## task,
## train,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [27400] {network,
## represent,
## task,
## result,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [27401] {network,
## represent,
## train,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27402] {network,
## represent,
## task,
## train,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [27403] {approach,
## represent,
## task,
## result,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27404] {approach,
## task,
## propos,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27405] {approach,
## represent,
## task,
## propos,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [27406] {represent,
## task,
## propos,
## result,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27407] {approach,
## represent,
## propos,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27408] {approach,
## represent,
## task,
## propos,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [27409] {data,
## task,
## dataset,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27410] {data,
## represent,
## task,
## dataset,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [27411] {data,
## represent,
## task,
## result,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27412] {represent,
## task,
## dataset,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27413] {data,
## represent,
## dataset,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27414] {data,
## represent,
## task,
## dataset,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [27415] {data,
## represent,
## task,
## result,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [27416] {data,
## show,
## task,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27417] {data,
## represent,
## show,
## task,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [27418] {represent,
## show,
## task,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27419] {data,
## represent,
## show,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27420] {data,
## represent,
## show,
## task,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [27421] {data,
## represent,
## task,
## result,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [27422] {data,
## featur,
## task,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27423] {data,
## featur,
## represent,
## task,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [27424] {featur,
## represent,
## task,
## result,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [27425] {data,
## featur,
## represent,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27426] {data,
## featur,
## represent,
## task,
## learn} => {result} 0.1000000 0.7500000 2.250000 3
## [27427] {data,
## represent,
## train,
## dataset,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [27428] {data,
## network,
## train,
## dataset,
## result} => {represent} 0.1000000 1.0000000 2.000000 3
## [27429] {data,
## network,
## represent,
## train,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27430] {network,
## represent,
## train,
## dataset,
## result} => {data} 0.1000000 1.0000000 2.307692 3
## [27431] {data,
## network,
## represent,
## dataset,
## result} => {train} 0.1000000 1.0000000 2.500000 3
## [27432] {data,
## network,
## represent,
## train,
## dataset} => {result} 0.1000000 1.0000000 3.000000 3
## [27433] {approach,
## algorithm,
## dataset,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27434] {approach,
## network,
## algorithm,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [27435] {approach,
## network,
## algorithm,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27436] {network,
## algorithm,
## dataset,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27437] {approach,
## network,
## dataset,
## neural,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [27438] {approach,
## network,
## algorithm,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [27439] {approach,
## train,
## dataset,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27440] {approach,
## network,
## train,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [27441] {approach,
## network,
## train,
## neural,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27442] {network,
## train,
## dataset,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27443] {approach,
## network,
## dataset,
## neural,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [27444] {approach,
## network,
## train,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [27445] {approach,
## show,
## dataset,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [27446] {approach,
## network,
## show,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [27447] {approach,
## network,
## dataset,
## neural,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [27448] {approach,
## network,
## show,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27449] {network,
## show,
## dataset,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27450] {approach,
## network,
## show,
## dataset,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [27451] {approach,
## featur,
## method,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [27452] {approach,
## method,
## network,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [27453] {approach,
## featur,
## method,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [27454] {featur,
## method,
## network,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [27455] {approach,
## featur,
## method,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [27456] {approach,
## featur,
## network,
## represent,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [27457] {approach,
## method,
## show,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [27458] {approach,
## method,
## dataset,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [27459] {approach,
## method,
## show,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27460] {approach,
## method,
## show,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [27461] {method,
## show,
## dataset,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27462] {approach,
## show,
## dataset,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [27463] {approach,
## method,
## show,
## dataset,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [27464] {approach,
## method,
## model,
## dataset,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [27465] {approach,
## method,
## model,
## show,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27466] {approach,
## method,
## model,
## show,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [27467] {method,
## model,
## show,
## dataset,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [27468] {approach,
## model,
## show,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [27469] {approach,
## method,
## show,
## dataset,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27470] {approach,
## method,
## model,
## dataset,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [27471] {approach,
## method,
## model,
## show,
## dataset} => {learn} 0.1000000 0.7500000 1.730769 3
## [27472] {approach,
## method,
## model,
## show,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27473] {method,
## model,
## show,
## dataset,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27474] {approach,
## model,
## show,
## dataset,
## learn} => {method} 0.1000000 1.0000000 2.727273 3
## [27475] {approach,
## method,
## show,
## dataset,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [27476] {approach,
## method,
## model,
## show,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [27477] {approach,
## method,
## model,
## dataset,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [27478] {approach,
## method,
## model,
## show,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27479] {method,
## model,
## show,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27480] {approach,
## model,
## show,
## dataset,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [27481] {approach,
## method,
## show,
## dataset,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [27482] {approach,
## method,
## network,
## show,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [27483] {approach,
## method,
## network,
## dataset,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [27484] {approach,
## method,
## network,
## show,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27485] {method,
## network,
## show,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27486] {approach,
## network,
## show,
## dataset,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [27487] {approach,
## method,
## model,
## show,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [27488] {approach,
## featur,
## method,
## show,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [27489] {approach,
## featur,
## method,
## model,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [27490] {approach,
## featur,
## method,
## model,
## show} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27491] {featur,
## method,
## model,
## show,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [27492] {approach,
## featur,
## model,
## show,
## dataset} => {method} 0.1000000 1.0000000 2.727273 3
## [27493] {approach,
## featur,
## method,
## model,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [27494] {approach,
## method,
## model,
## network,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [27495] {approach,
## featur,
## method,
## network,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [27496] {approach,
## featur,
## method,
## model,
## network} => {show} 0.1000000 1.0000000 1.875000 3
## [27497] {featur,
## method,
## model,
## network,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [27498] {approach,
## featur,
## model,
## network,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [27499] {data,
## represent,
## show,
## task,
## train} => {network} 0.1000000 1.0000000 1.578947 3
## [27500] {data,
## network,
## represent,
## task,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [27501] {data,
## network,
## show,
## task,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [27502] {network,
## represent,
## show,
## task,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [27503] {data,
## network,
## represent,
## show,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [27504] {data,
## network,
## represent,
## show,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [27505] {approach,
## data,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27506] {approach,
## data,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27507] {approach,
## task,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27508] {approach,
## data,
## task,
## dataset,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27509] {data,
## task,
## dataset,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [27510] {approach,
## data,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27511] {approach,
## data,
## task,
## dataset,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [27512] {approach,
## data,
## represent,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27513] {approach,
## represent,
## task,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [27514] {approach,
## data,
## represent,
## task,
## dataset} => {work} 0.1000000 1.0000000 2.500000 3
## [27515] {data,
## represent,
## task,
## dataset,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [27516] {approach,
## data,
## represent,
## dataset,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [27517] {approach,
## data,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27518] {approach,
## data,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27519] {approach,
## task,
## dataset,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [27520] {approach,
## data,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [27521] {data,
## task,
## dataset,
## propos,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [27522] {approach,
## data,
## dataset,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [27523] {approach,
## data,
## task,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27524] {approach,
## data,
## represent,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27525] {approach,
## represent,
## task,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27526] {approach,
## data,
## represent,
## task,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27527] {data,
## represent,
## task,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27528] {approach,
## data,
## represent,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27529] {approach,
## data,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27530] {approach,
## data,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27531] {approach,
## task,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27532] {approach,
## data,
## task,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27533] {data,
## task,
## propos,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [27534] {approach,
## data,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27535] {approach,
## data,
## represent,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27536] {approach,
## data,
## task,
## propos,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [27537] {approach,
## represent,
## task,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [27538] {approach,
## data,
## represent,
## task,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [27539] {data,
## represent,
## task,
## propos,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [27540] {approach,
## data,
## represent,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [27541] {approach,
## task,
## dataset,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27542] {approach,
## represent,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27543] {approach,
## represent,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27544] {approach,
## represent,
## task,
## dataset,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27545] {represent,
## task,
## dataset,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27546] {approach,
## represent,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27547] {approach,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27548] {approach,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27549] {approach,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27550] {approach,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27551] {task,
## dataset,
## propos,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [27552] {approach,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27553] {approach,
## represent,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27554] {approach,
## task,
## dataset,
## propos,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [27555] {approach,
## represent,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27556] {approach,
## represent,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [27557] {represent,
## task,
## dataset,
## propos,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [27558] {approach,
## represent,
## dataset,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [27559] {approach,
## represent,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27560] {approach,
## task,
## propos,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27561] {approach,
## represent,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27562] {approach,
## represent,
## task,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27563] {represent,
## task,
## propos,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27564] {approach,
## represent,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27565] {approach,
## data,
## task,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27566] {approach,
## data,
## represent,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [27567] {approach,
## data,
## represent,
## task,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27568] {approach,
## represent,
## task,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27569] {data,
## represent,
## task,
## dataset,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [27570] {approach,
## data,
## represent,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27571] {approach,
## data,
## task,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27572] {approach,
## data,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27573] {approach,
## data,
## task,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27574] {approach,
## task,
## dataset,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27575] {data,
## task,
## dataset,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [27576] {approach,
## data,
## dataset,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27577] {approach,
## data,
## represent,
## task,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [27578] {approach,
## data,
## task,
## dataset,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [27579] {approach,
## data,
## represent,
## task,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27580] {approach,
## represent,
## task,
## dataset,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [27581] {data,
## represent,
## task,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27582] {approach,
## data,
## represent,
## dataset,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [27583] {approach,
## data,
## represent,
## task,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27584] {approach,
## data,
## task,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27585] {approach,
## data,
## represent,
## task,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [27586] {approach,
## represent,
## task,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [27587] {data,
## represent,
## task,
## propos,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27588] {approach,
## data,
## represent,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27589] {approach,
## data,
## represent,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [27590] {approach,
## data,
## network,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [27591] {approach,
## data,
## network,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [27592] {approach,
## network,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [27593] {data,
## network,
## represent,
## show,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [27594] {approach,
## data,
## network,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [27595] {approach,
## data,
## represent,
## task,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [27596] {approach,
## data,
## featur,
## represent,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [27597] {approach,
## data,
## featur,
## task,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [27598] {approach,
## featur,
## represent,
## task,
## propos} => {data} 0.1000000 0.7500000 1.730769 3
## [27599] {data,
## featur,
## represent,
## task,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [27600] {approach,
## data,
## featur,
## represent,
## propos} => {task} 0.1000000 1.0000000 2.727273 3
## [27601] {approach,
## data,
## model,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [27602] {approach,
## data,
## featur,
## represent,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [27603] {approach,
## data,
## featur,
## model,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [27604] {approach,
## featur,
## model,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [27605] {data,
## featur,
## model,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [27606] {approach,
## data,
## featur,
## model,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [27607] {approach,
## represent,
## task,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27608] {approach,
## task,
## dataset,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27609] {approach,
## represent,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27610] {approach,
## represent,
## task,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27611] {represent,
## task,
## dataset,
## propos,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27612] {approach,
## represent,
## dataset,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [27613] {approach,
## represent,
## task,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [27614] {approach,
## featur,
## represent,
## task,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27615] {approach,
## featur,
## task,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27616] {approach,
## featur,
## represent,
## task,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [27617] {featur,
## represent,
## task,
## propos,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27618] {approach,
## featur,
## represent,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27619] {approach,
## represent,
## task,
## propos,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [27620] {approach,
## network,
## represent,
## task,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27621] {approach,
## network,
## task,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27622] {approach,
## network,
## represent,
## task,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27623] {network,
## represent,
## task,
## propos,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27624] {approach,
## network,
## represent,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27625] {data,
## task,
## dataset,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [27626] {data,
## represent,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27627] {data,
## represent,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27628] {represent,
## task,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27629] {data,
## represent,
## task,
## dataset,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27630] {data,
## represent,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27631] {data,
## task,
## dataset,
## work,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [27632] {data,
## task,
## dataset,
## propos,
## work} => {learn} 0.1333333 1.0000000 2.307692 4
## [27633] {data,
## task,
## propos,
## work,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [27634] {task,
## dataset,
## propos,
## work,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [27635] {data,
## task,
## dataset,
## propos,
## learn} => {work} 0.1333333 1.0000000 2.500000 4
## [27636] {data,
## dataset,
## propos,
## work,
## learn} => {task} 0.1333333 1.0000000 2.727273 4
## [27637] {data,
## task,
## dataset,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [27638] {data,
## model,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27639] {data,
## model,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27640] {model,
## task,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27641] {data,
## model,
## task,
## dataset,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27642] {data,
## model,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27643] {data,
## task,
## dataset,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [27644] {data,
## featur,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27645] {data,
## featur,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27646] {featur,
## task,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27647] {data,
## featur,
## task,
## dataset,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27648] {data,
## featur,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27649] {data,
## task,
## dataset,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [27650] {data,
## network,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27651] {data,
## network,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27652] {network,
## task,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27653] {data,
## network,
## task,
## dataset,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27654] {data,
## network,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27655] {data,
## represent,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27656] {data,
## task,
## dataset,
## propos,
## work} => {represent} 0.1000000 0.7500000 1.500000 3
## [27657] {data,
## represent,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27658] {represent,
## task,
## dataset,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [27659] {data,
## represent,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [27660] {data,
## represent,
## dataset,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [27661] {data,
## task,
## dataset,
## propos,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [27662] {data,
## model,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27663] {data,
## model,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27664] {model,
## task,
## dataset,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [27665] {data,
## model,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [27666] {data,
## model,
## dataset,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [27667] {data,
## task,
## dataset,
## propos,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [27668] {data,
## featur,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27669] {data,
## featur,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27670] {featur,
## task,
## dataset,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [27671] {data,
## featur,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [27672] {data,
## featur,
## dataset,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [27673] {data,
## task,
## dataset,
## propos,
## work} => {network} 0.1000000 0.7500000 1.184211 3
## [27674] {data,
## network,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27675] {data,
## network,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27676] {network,
## task,
## dataset,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [27677] {data,
## network,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [27678] {data,
## network,
## dataset,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [27679] {data,
## model,
## task,
## dataset,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [27680] {data,
## featur,
## task,
## dataset,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [27681] {data,
## featur,
## model,
## task,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27682] {featur,
## model,
## task,
## dataset,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [27683] {data,
## featur,
## model,
## task,
## dataset} => {work} 0.1000000 1.0000000 2.500000 3
## [27684] {data,
## featur,
## model,
## dataset,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [27685] {data,
## represent,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27686] {data,
## task,
## propos,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [27687] {data,
## represent,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27688] {represent,
## task,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27689] {data,
## represent,
## task,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27690] {data,
## represent,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27691] {data,
## task,
## propos,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [27692] {data,
## model,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27693] {data,
## model,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27694] {model,
## task,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27695] {data,
## model,
## task,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27696] {data,
## model,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27697] {data,
## task,
## propos,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [27698] {data,
## featur,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27699] {data,
## featur,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27700] {featur,
## task,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27701] {data,
## featur,
## task,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27702] {data,
## featur,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27703] {data,
## task,
## propos,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [27704] {data,
## network,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27705] {data,
## network,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27706] {network,
## task,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27707] {data,
## network,
## task,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27708] {data,
## network,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27709] {data,
## model,
## task,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27710] {data,
## featur,
## task,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27711] {data,
## featur,
## model,
## task,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27712] {featur,
## model,
## task,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27713] {data,
## featur,
## model,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27714] {data,
## model,
## task,
## propos,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [27715] {data,
## featur,
## task,
## propos,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [27716] {data,
## featur,
## model,
## task,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27717] {featur,
## model,
## task,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [27718] {data,
## featur,
## model,
## task,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [27719] {data,
## featur,
## model,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [27720] {represent,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27721] {task,
## dataset,
## propos,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [27722] {represent,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27723] {represent,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27724] {represent,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27725] {represent,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27726] {task,
## dataset,
## propos,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [27727] {model,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27728] {model,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27729] {model,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27730] {model,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27731] {model,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27732] {task,
## dataset,
## propos,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [27733] {featur,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27734] {featur,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27735] {featur,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27736] {featur,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27737] {featur,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27738] {task,
## dataset,
## propos,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [27739] {network,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27740] {network,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27741] {network,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27742] {network,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27743] {network,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27744] {model,
## task,
## dataset,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27745] {featur,
## task,
## dataset,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27746] {featur,
## model,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27747] {featur,
## model,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27748] {featur,
## model,
## task,
## dataset,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27749] {featur,
## model,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27750] {model,
## task,
## dataset,
## propos,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [27751] {featur,
## task,
## dataset,
## propos,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [27752] {featur,
## model,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27753] {featur,
## model,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27754] {featur,
## model,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [27755] {featur,
## model,
## dataset,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [27756] {model,
## task,
## propos,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27757] {featur,
## task,
## propos,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27758] {featur,
## model,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27759] {featur,
## model,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27760] {featur,
## model,
## task,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27761] {featur,
## model,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27762] {data,
## represent,
## task,
## dataset,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [27763] {data,
## show,
## task,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27764] {data,
## represent,
## show,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [27765] {data,
## represent,
## show,
## task,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27766] {represent,
## show,
## task,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27767] {data,
## represent,
## show,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27768] {data,
## represent,
## task,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [27769] {data,
## task,
## dataset,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [27770] {data,
## represent,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27771] {data,
## represent,
## task,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27772] {represent,
## task,
## dataset,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27773] {data,
## represent,
## dataset,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [27774] {data,
## represent,
## task,
## dataset,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [27775] {data,
## featur,
## task,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [27776] {data,
## featur,
## represent,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [27777] {data,
## featur,
## represent,
## task,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27778] {featur,
## represent,
## task,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27779] {data,
## featur,
## represent,
## dataset,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [27780] {data,
## represent,
## task,
## dataset,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [27781] {data,
## network,
## task,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [27782] {data,
## network,
## represent,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [27783] {data,
## network,
## represent,
## task,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27784] {network,
## represent,
## task,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27785] {data,
## network,
## represent,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27786] {data,
## show,
## task,
## dataset,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [27787] {data,
## network,
## task,
## dataset,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [27788] {data,
## network,
## show,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [27789] {data,
## network,
## show,
## task,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27790] {network,
## show,
## task,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27791] {data,
## network,
## show,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27792] {data,
## task,
## dataset,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [27793] {data,
## model,
## task,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27794] {data,
## model,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27795] {data,
## model,
## task,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27796] {model,
## task,
## dataset,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27797] {data,
## model,
## dataset,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [27798] {data,
## task,
## dataset,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [27799] {data,
## featur,
## task,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [27800] {data,
## featur,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27801] {data,
## featur,
## task,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27802] {featur,
## task,
## dataset,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27803] {data,
## featur,
## dataset,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [27804] {data,
## task,
## dataset,
## propos,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [27805] {data,
## network,
## task,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [27806] {data,
## network,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27807] {data,
## network,
## task,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27808] {network,
## task,
## dataset,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27809] {data,
## network,
## dataset,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27810] {data,
## model,
## task,
## dataset,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27811] {data,
## featur,
## task,
## dataset,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [27812] {data,
## featur,
## model,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [27813] {featur,
## model,
## task,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27814] {data,
## featur,
## model,
## dataset,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [27815] {data,
## featur,
## task,
## dataset,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [27816] {data,
## network,
## task,
## dataset,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [27817] {data,
## featur,
## network,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [27818] {data,
## featur,
## network,
## task,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27819] {featur,
## network,
## task,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27820] {data,
## featur,
## network,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27821] {data,
## represent,
## show,
## task,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [27822] {data,
## network,
## represent,
## task,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [27823] {data,
## network,
## show,
## task,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [27824] {data,
## network,
## represent,
## show,
## task} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27825] {network,
## represent,
## show,
## task,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [27826] {data,
## network,
## represent,
## show,
## dataset} => {task} 0.1000000 1.0000000 2.727273 3
## [27827] {data,
## model,
## task,
## dataset,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [27828] {data,
## featur,
## task,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [27829] {data,
## featur,
## model,
## task,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [27830] {data,
## featur,
## model,
## task,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27831] {featur,
## model,
## task,
## dataset,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [27832] {data,
## featur,
## model,
## dataset,
## propos} => {task} 0.1000000 0.7500000 2.045455 3
## [27833] {data,
## represent,
## show,
## task,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [27834] {data,
## featur,
## represent,
## task,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [27835] {data,
## featur,
## show,
## task,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [27836] {data,
## featur,
## represent,
## show,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [27837] {featur,
## represent,
## show,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27838] {data,
## featur,
## represent,
## show,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27839] {data,
## represent,
## show,
## task,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [27840] {data,
## network,
## represent,
## task,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [27841] {data,
## network,
## show,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27842] {data,
## network,
## represent,
## show,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [27843] {network,
## represent,
## show,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27844] {data,
## network,
## represent,
## show,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27845] {data,
## model,
## represent,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27846] {data,
## featur,
## represent,
## task,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [27847] {data,
## featur,
## model,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [27848] {featur,
## model,
## represent,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27849] {data,
## featur,
## model,
## represent,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [27850] {data,
## model,
## show,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27851] {data,
## featur,
## show,
## task,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [27852] {data,
## featur,
## model,
## show,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [27853] {featur,
## model,
## show,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27854] {data,
## featur,
## model,
## show,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27855] {data,
## model,
## task,
## propos,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [27856] {data,
## featur,
## task,
## propos,
## learn} => {model} 0.1333333 1.0000000 1.875000 4
## [27857] {data,
## featur,
## model,
## task,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [27858] {data,
## featur,
## model,
## task,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [27859] {featur,
## model,
## task,
## propos,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [27860] {data,
## featur,
## model,
## propos,
## learn} => {task} 0.1333333 0.8000000 2.181818 4
## [27861] {data,
## model,
## represent,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [27862] {data,
## featur,
## represent,
## show,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [27863] {data,
## featur,
## model,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [27864] {data,
## featur,
## model,
## show,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [27865] {featur,
## model,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [27866] {data,
## featur,
## model,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [27867] {data,
## featur,
## represent,
## show,
## task} => {network} 0.1000000 0.7500000 1.184211 3
## [27868] {data,
## network,
## represent,
## show,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [27869] {data,
## featur,
## network,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [27870] {data,
## featur,
## network,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [27871] {featur,
## network,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [27872] {data,
## featur,
## network,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [27873] {represent,
## show,
## task,
## dataset,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [27874] {network,
## represent,
## task,
## dataset,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [27875] {network,
## show,
## task,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27876] {network,
## represent,
## show,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [27877] {network,
## represent,
## show,
## task,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27878] {network,
## represent,
## show,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [27879] {model,
## task,
## dataset,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27880] {featur,
## task,
## dataset,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27881] {featur,
## model,
## task,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27882] {featur,
## model,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27883] {featur,
## model,
## task,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [27884] {featur,
## model,
## dataset,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [27885] {approach,
## data,
## dataset,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27886] {approach,
## data,
## represent,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27887] {approach,
## data,
## represent,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27888] {approach,
## represent,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27889] {approach,
## data,
## represent,
## dataset,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27890] {data,
## represent,
## dataset,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27891] {approach,
## data,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27892] {approach,
## data,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27893] {approach,
## data,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27894] {approach,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27895] {approach,
## data,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27896] {data,
## dataset,
## propos,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [27897] {approach,
## data,
## represent,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27898] {approach,
## data,
## dataset,
## propos,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [27899] {approach,
## data,
## represent,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27900] {approach,
## represent,
## dataset,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [27901] {approach,
## data,
## represent,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [27902] {data,
## represent,
## dataset,
## propos,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [27903] {approach,
## data,
## represent,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27904] {approach,
## data,
## propos,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27905] {approach,
## data,
## represent,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27906] {approach,
## represent,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27907] {approach,
## data,
## represent,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27908] {data,
## represent,
## propos,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27909] {approach,
## represent,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27910] {approach,
## dataset,
## propos,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27911] {approach,
## represent,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27912] {approach,
## represent,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27913] {approach,
## represent,
## dataset,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27914] {represent,
## dataset,
## propos,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27915] {approach,
## show,
## dataset,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [27916] {approach,
## network,
## show,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27917] {approach,
## network,
## dataset,
## propos,
## work} => {show} 0.1000000 1.0000000 1.875000 3
## [27918] {approach,
## network,
## show,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27919] {approach,
## network,
## show,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [27920] {network,
## show,
## dataset,
## propos,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [27921] {approach,
## represent,
## show,
## work,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [27922] {approach,
## network,
## represent,
## work,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [27923] {approach,
## network,
## show,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27924] {approach,
## network,
## represent,
## show,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27925] {approach,
## network,
## represent,
## show,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27926] {network,
## represent,
## show,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27927] {approach,
## model,
## represent,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27928] {approach,
## featur,
## represent,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27929] {approach,
## featur,
## model,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27930] {approach,
## featur,
## model,
## represent,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27931] {approach,
## featur,
## model,
## represent,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27932] {featur,
## model,
## represent,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27933] {approach,
## data,
## represent,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27934] {approach,
## data,
## dataset,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27935] {approach,
## data,
## represent,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27936] {approach,
## data,
## represent,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27937] {approach,
## represent,
## dataset,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [27938] {data,
## represent,
## dataset,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [27939] {approach,
## represent,
## show,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27940] {approach,
## represent,
## dataset,
## propos,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [27941] {approach,
## show,
## dataset,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27942] {approach,
## represent,
## show,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27943] {approach,
## represent,
## show,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27944] {represent,
## show,
## dataset,
## propos,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [27945] {approach,
## represent,
## dataset,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [27946] {approach,
## model,
## represent,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27947] {approach,
## model,
## dataset,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [27948] {approach,
## model,
## represent,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [27949] {approach,
## model,
## represent,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27950] {model,
## represent,
## dataset,
## propos,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [27951] {approach,
## featur,
## model,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [27952] {approach,
## model,
## network,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [27953] {approach,
## featur,
## network,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [27954] {approach,
## featur,
## model,
## network,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [27955] {approach,
## featur,
## model,
## network,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [27956] {featur,
## model,
## network,
## represent,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [27957] {data,
## represent,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27958] {data,
## dataset,
## propos,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [27959] {data,
## represent,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27960] {data,
## represent,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27961] {represent,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27962] {data,
## represent,
## dataset,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27963] {data,
## dataset,
## propos,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [27964] {data,
## model,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27965] {data,
## model,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27966] {data,
## model,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27967] {model,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27968] {data,
## model,
## dataset,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27969] {data,
## dataset,
## propos,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [27970] {data,
## featur,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27971] {data,
## featur,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27972] {data,
## featur,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27973] {featur,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27974] {data,
## featur,
## dataset,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27975] {data,
## dataset,
## propos,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [27976] {data,
## network,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27977] {data,
## network,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27978] {data,
## network,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27979] {network,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27980] {data,
## network,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [27981] {data,
## model,
## dataset,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27982] {data,
## featur,
## dataset,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27983] {data,
## featur,
## model,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27984] {data,
## featur,
## model,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27985] {featur,
## model,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27986] {data,
## featur,
## model,
## dataset,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [27987] {data,
## model,
## dataset,
## propos,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [27988] {data,
## featur,
## dataset,
## propos,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [27989] {data,
## featur,
## model,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [27990] {data,
## featur,
## model,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [27991] {featur,
## model,
## dataset,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [27992] {data,
## featur,
## model,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [27993] {data,
## model,
## propos,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27994] {data,
## featur,
## propos,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [27995] {data,
## featur,
## model,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [27996] {data,
## featur,
## model,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [27997] {featur,
## model,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [27998] {model,
## dataset,
## propos,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [27999] {featur,
## dataset,
## propos,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28000] {featur,
## model,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28001] {featur,
## model,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28002] {featur,
## model,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28003] {featur,
## model,
## dataset,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [28004] {data,
## model,
## perform,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28005] {data,
## featur,
## perform,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28006] {data,
## featur,
## model,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28007] {data,
## featur,
## model,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [28008] {featur,
## model,
## perform,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28009] {represent,
## dataset,
## perform,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28010] {model,
## represent,
## dataset,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28011] {model,
## dataset,
## perform,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28012] {model,
## represent,
## dataset,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [28013] {model,
## represent,
## perform,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28014] {model,
## represent,
## dataset,
## propos,
## learn} => {perform} 0.1000000 0.7500000 1.607143 3
## [28015] {data,
## represent,
## show,
## dataset,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [28016] {data,
## network,
## represent,
## dataset,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [28017] {data,
## network,
## show,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28018] {data,
## network,
## represent,
## show,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [28019] {data,
## network,
## represent,
## show,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28020] {network,
## represent,
## show,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28021] {data,
## represent,
## dataset,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [28022] {data,
## model,
## represent,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28023] {data,
## model,
## dataset,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [28024] {data,
## model,
## represent,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [28025] {data,
## model,
## represent,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28026] {model,
## represent,
## dataset,
## propos,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [28027] {data,
## represent,
## dataset,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [28028] {data,
## featur,
## represent,
## dataset,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [28029] {data,
## featur,
## dataset,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [28030] {data,
## featur,
## represent,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [28031] {data,
## featur,
## represent,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28032] {featur,
## represent,
## dataset,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28033] {data,
## model,
## represent,
## dataset,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28034] {data,
## featur,
## represent,
## dataset,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [28035] {data,
## featur,
## model,
## dataset,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [28036] {data,
## featur,
## model,
## represent,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [28037] {data,
## featur,
## model,
## represent,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [28038] {featur,
## model,
## represent,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28039] {data,
## model,
## dataset,
## propos,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [28040] {data,
## featur,
## dataset,
## propos,
## learn} => {model} 0.1333333 1.0000000 1.875000 4
## [28041] {data,
## featur,
## model,
## dataset,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [28042] {data,
## featur,
## model,
## dataset,
## propos} => {learn} 0.1333333 1.0000000 2.307692 4
## [28043] {data,
## featur,
## model,
## propos,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [28044] {featur,
## model,
## dataset,
## propos,
## learn} => {data} 0.1333333 1.0000000 2.307692 4
## [28045] {data,
## model,
## represent,
## dataset,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [28046] {data,
## featur,
## represent,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [28047] {data,
## featur,
## model,
## represent,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [28048] {data,
## featur,
## model,
## dataset,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [28049] {data,
## featur,
## model,
## represent,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28050] {featur,
## model,
## represent,
## dataset,
## propos} => {data} 0.1000000 1.0000000 2.307692 3
## [28051] {data,
## model,
## represent,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28052] {data,
## featur,
## represent,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28053] {data,
## featur,
## model,
## represent,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [28054] {data,
## featur,
## model,
## represent,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [28055] {featur,
## model,
## represent,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28056] {model,
## represent,
## dataset,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [28057] {featur,
## represent,
## dataset,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28058] {featur,
## model,
## represent,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28059] {featur,
## model,
## dataset,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [28060] {featur,
## model,
## represent,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [28061] {featur,
## model,
## represent,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28062] {data,
## model,
## dataset,
## propos,
## learn,
## handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [28063] {data,
## featur,
## dataset,
## propos,
## learn,
## handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [28064] {data,
## featur,
## model,
## dataset,
## learn,
## handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [28065] {data,
## featur,
## model,
## dataset,
## propos,
## handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [28066] {data,
## featur,
## model,
## propos,
## learn,
## handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28067] {featur,
## model,
## dataset,
## propos,
## learn,
## handcraft} => {data} 0.1000000 1.0000000 2.307692 3
## [28068] {data,
## featur,
## model,
## dataset,
## propos,
## learn} => {handcraft} 0.1000000 0.7500000 7.500000 3
## [28069] {data,
## task,
## dataset,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [28070] {data,
## featur,
## task,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [28071] {data,
## featur,
## task,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28072] {featur,
## task,
## dataset,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [28073] {data,
## featur,
## dataset,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [28074] {data,
## featur,
## task,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [28075] {data,
## featur,
## task,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [28076] {data,
## task,
## dataset,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [28077] {data,
## network,
## task,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [28078] {data,
## network,
## task,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28079] {network,
## task,
## dataset,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [28080] {data,
## network,
## dataset,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [28081] {data,
## network,
## task,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [28082] {data,
## network,
## task,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [28083] {data,
## featur,
## task,
## dataset,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [28084] {data,
## network,
## task,
## dataset,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [28085] {data,
## featur,
## network,
## task,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28086] {featur,
## network,
## task,
## dataset,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [28087] {data,
## featur,
## network,
## dataset,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [28088] {data,
## featur,
## network,
## task,
## dataset,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [28089] {data,
## featur,
## network,
## task,
## dataset,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [28090] {data,
## featur,
## task,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [28091] {data,
## network,
## task,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [28092] {data,
## featur,
## network,
## task,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [28093] {featur,
## network,
## task,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [28094] {data,
## featur,
## network,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [28095] {data,
## featur,
## network,
## task,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [28096] {data,
## featur,
## network,
## task,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [28097] {featur,
## task,
## dataset,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [28098] {network,
## task,
## dataset,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [28099] {featur,
## network,
## task,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [28100] {featur,
## network,
## task,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28101] {featur,
## network,
## dataset,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [28102] {featur,
## network,
## task,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [28103] {featur,
## network,
## task,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [28104] {data,
## featur,
## dataset,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [28105] {data,
## network,
## dataset,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [28106] {data,
## featur,
## network,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [28107] {data,
## featur,
## network,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28108] {featur,
## network,
## dataset,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [28109] {data,
## featur,
## network,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [28110] {data,
## featur,
## network,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [28111] {data,
## featur,
## task,
## dataset,
## learn,
## care} => {network} 0.1000000 1.0000000 1.578947 3
## [28112] {data,
## network,
## task,
## dataset,
## learn,
## care} => {featur} 0.1000000 1.0000000 1.875000 3
## [28113] {data,
## featur,
## network,
## task,
## dataset,
## care} => {learn} 0.1000000 1.0000000 2.307692 3
## [28114] {data,
## featur,
## network,
## task,
## learn,
## care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28115] {featur,
## network,
## task,
## dataset,
## learn,
## care} => {data} 0.1000000 1.0000000 2.307692 3
## [28116] {data,
## featur,
## network,
## dataset,
## learn,
## care} => {task} 0.1000000 1.0000000 2.727273 3
## [28117] {data,
## featur,
## network,
## task,
## dataset,
## learn} => {care} 0.1000000 1.0000000 10.000000 3
## [28118] {boltzmann,
## machin,
## restrict,
## show,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [28119] {boltzmann,
## machin,
## model,
## restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [28120] {boltzmann,
## machin,
## model,
## restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [28121] {boltzmann,
## model,
## restrict,
## show,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [28122] {boltzmann,
## machin,
## model,
## restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [28123] {boltzmann,
## machin,
## model,
## show,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [28124] {machin,
## model,
## restrict,
## show,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [28125] {boltzmann,
## data,
## machin,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [28126] {boltzmann,
## data,
## machin,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [28127] {boltzmann,
## machin,
## model,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [28128] {boltzmann,
## data,
## machin,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [28129] {boltzmann,
## data,
## model,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [28130] {boltzmann,
## data,
## machin,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [28131] {data,
## machin,
## model,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [28132] {boltzmann,
## data,
## machin,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [28133] {boltzmann,
## data,
## featur,
## machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [28134] {boltzmann,
## featur,
## machin,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [28135] {boltzmann,
## data,
## featur,
## machin,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [28136] {boltzmann,
## data,
## featur,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [28137] {boltzmann,
## data,
## featur,
## machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [28138] {data,
## featur,
## machin,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [28139] {boltzmann,
## data,
## machin,
## model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [28140] {boltzmann,
## data,
## featur,
## machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [28141] {boltzmann,
## featur,
## machin,
## model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [28142] {boltzmann,
## data,
## featur,
## machin,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [28143] {boltzmann,
## data,
## featur,
## model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [28144] {boltzmann,
## data,
## featur,
## machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [28145] {data,
## featur,
## machin,
## model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [28146] {boltzmann,
## machin,
## model,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [28147] {boltzmann,
## featur,
## machin,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [28148] {boltzmann,
## featur,
## machin,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [28149] {boltzmann,
## featur,
## machin,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [28150] {boltzmann,
## featur,
## model,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [28151] {boltzmann,
## featur,
## machin,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [28152] {featur,
## machin,
## model,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [28153] {boltzmann,
## data,
## machin,
## model,
## restrict,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [28154] {boltzmann,
## data,
## featur,
## machin,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [28155] {boltzmann,
## data,
## featur,
## machin,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [28156] {boltzmann,
## featur,
## machin,
## model,
## restrict,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [28157] {boltzmann,
## data,
## featur,
## model,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [28158] {boltzmann,
## data,
## featur,
## machin,
## model,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [28159] {data,
## featur,
## machin,
## model,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [28160] {boltzmann,
## data,
## model,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [28161] {boltzmann,
## data,
## featur,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [28162] {boltzmann,
## data,
## featur,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [28163] {boltzmann,
## featur,
## model,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [28164] {boltzmann,
## data,
## featur,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [28165] {boltzmann,
## data,
## featur,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [28166] {data,
## featur,
## model,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [28167] {boltzmann,
## data,
## machin,
## model,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [28168] {boltzmann,
## data,
## featur,
## machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [28169] {boltzmann,
## data,
## featur,
## machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [28170] {boltzmann,
## featur,
## machin,
## model,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [28171] {boltzmann,
## data,
## featur,
## machin,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [28172] {boltzmann,
## data,
## featur,
## model,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [28173] {data,
## featur,
## machin,
## model,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [28174] {data,
## machin,
## model,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [28175] {data,
## featur,
## machin,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [28176] {data,
## featur,
## machin,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [28177] {featur,
## machin,
## model,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [28178] {data,
## featur,
## machin,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [28179] {data,
## featur,
## model,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [28180] {data,
## featur,
## machin,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [28181] {approach,
## method,
## show,
## dataset,
## learn,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [28182] {approach,
## method,
## model,
## dataset,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [28183] {approach,
## method,
## model,
## show,
## dataset,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [28184] {approach,
## method,
## model,
## show,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28185] {method,
## model,
## show,
## dataset,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [28186] {approach,
## model,
## show,
## dataset,
## learn,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [28187] {approach,
## method,
## model,
## show,
## dataset,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [28188] {approach,
## represent,
## show,
## dataset,
## learn,
## sourc} => {propos} 0.1000000 1.0000000 2.000000 3
## [28189] {approach,
## represent,
## dataset,
## propos,
## learn,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [28190] {approach,
## show,
## dataset,
## propos,
## learn,
## sourc} => {represent} 0.1000000 1.0000000 2.000000 3
## [28191] {approach,
## represent,
## show,
## dataset,
## propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [28192] {approach,
## represent,
## show,
## propos,
## learn,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28193] {represent,
## show,
## dataset,
## propos,
## learn,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [28194] {approach,
## represent,
## show,
## dataset,
## propos,
## learn} => {sourc} 0.1000000 1.0000000 7.500000 3
## [28195] {approach,
## data,
## import,
## model,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [28196] {approach,
## data,
## featur,
## import,
## represent,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [28197] {approach,
## data,
## featur,
## import,
## model,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [28198] {approach,
## featur,
## import,
## model,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [28199] {data,
## featur,
## import,
## model,
## represent,
## task} => {approach} 0.1000000 0.7500000 1.875000 3
## [28200] {approach,
## data,
## featur,
## import,
## model,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [28201] {approach,
## data,
## featur,
## model,
## represent,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [28202] {data,
## import,
## model,
## represent,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28203] {data,
## featur,
## import,
## represent,
## task,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28204] {data,
## featur,
## import,
## model,
## task,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28205] {data,
## featur,
## import,
## model,
## represent,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [28206] {featur,
## import,
## model,
## represent,
## task,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28207] {data,
## featur,
## import,
## model,
## represent,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28208] {data,
## featur,
## model,
## represent,
## task,
## learn} => {import} 0.1000000 1.0000000 7.500000 3
## [28209] {data,
## import,
## model,
## represent,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [28210] {data,
## featur,
## import,
## represent,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [28211] {data,
## featur,
## import,
## model,
## represent,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [28212] {data,
## featur,
## import,
## model,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [28213] {featur,
## import,
## model,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [28214] {data,
## featur,
## import,
## model,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [28215] {data,
## featur,
## model,
## represent,
## show,
## task} => {import} 0.1000000 1.0000000 7.500000 3
## [28216] {perform,
## propos,
## problem,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [28217] {model,
## perform,
## problem,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [28218] {model,
## perform,
## propos,
## problem,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [28219] {model,
## propos,
## problem,
## learn,
## art,
## state} => {perform} 0.1000000 1.0000000 2.142857 3
## [28220] {model,
## perform,
## propos,
## learn,
## art,
## state} => {problem} 0.1000000 1.0000000 3.333333 3
## [28221] {model,
## perform,
## propos,
## problem,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [28222] {model,
## perform,
## propos,
## problem,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [28223] {data,
## task,
## propos,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [28224] {data,
## model,
## task,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [28225] {data,
## model,
## task,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [28226] {model,
## task,
## propos,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [28227] {data,
## model,
## propos,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [28228] {data,
## model,
## task,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [28229] {data,
## model,
## task,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [28230] {data,
## task,
## propos,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [28231] {data,
## featur,
## task,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [28232] {data,
## featur,
## task,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [28233] {featur,
## task,
## propos,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [28234] {data,
## featur,
## propos,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [28235] {data,
## featur,
## task,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [28236] {data,
## featur,
## task,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [28237] {data,
## model,
## task,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [28238] {data,
## featur,
## task,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [28239] {data,
## featur,
## model,
## task,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [28240] {featur,
## model,
## task,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [28241] {data,
## featur,
## model,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [28242] {data,
## featur,
## model,
## task,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [28243] {data,
## featur,
## model,
## task,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [28244] {data,
## model,
## task,
## propos,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [28245] {data,
## featur,
## task,
## propos,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [28246] {data,
## featur,
## model,
## task,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [28247] {featur,
## model,
## task,
## propos,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [28248] {data,
## featur,
## model,
## propos,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [28249] {data,
## featur,
## model,
## task,
## propos,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [28250] {data,
## featur,
## model,
## task,
## propos,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [28251] {model,
## task,
## propos,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [28252] {featur,
## task,
## propos,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [28253] {featur,
## model,
## task,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [28254] {featur,
## model,
## task,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [28255] {featur,
## model,
## propos,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [28256] {featur,
## model,
## task,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [28257] {featur,
## model,
## task,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [28258] {data,
## model,
## propos,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [28259] {data,
## featur,
## propos,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [28260] {data,
## featur,
## model,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [28261] {data,
## featur,
## model,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [28262] {featur,
## model,
## propos,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [28263] {data,
## featur,
## model,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [28264] {data,
## featur,
## model,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [28265] {data,
## model,
## task,
## propos,
## learn,
## art} => {featur} 0.1000000 1.0000000 1.875000 3
## [28266] {data,
## featur,
## task,
## propos,
## learn,
## art} => {model} 0.1000000 1.0000000 1.875000 3
## [28267] {data,
## featur,
## model,
## task,
## learn,
## art} => {propos} 0.1000000 1.0000000 2.000000 3
## [28268] {data,
## featur,
## model,
## task,
## propos,
## art} => {learn} 0.1000000 1.0000000 2.307692 3
## [28269] {featur,
## model,
## task,
## propos,
## learn,
## art} => {data} 0.1000000 1.0000000 2.307692 3
## [28270] {data,
## featur,
## model,
## propos,
## learn,
## art} => {task} 0.1000000 1.0000000 2.727273 3
## [28271] {data,
## featur,
## model,
## task,
## propos,
## learn} => {art} 0.1000000 0.7500000 5.625000 3
## [28272] {data,
## model,
## task,
## propos,
## learn,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [28273] {data,
## featur,
## task,
## propos,
## learn,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [28274] {data,
## featur,
## model,
## task,
## learn,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [28275] {data,
## featur,
## model,
## task,
## propos,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [28276] {featur,
## model,
## task,
## propos,
## learn,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [28277] {data,
## featur,
## model,
## propos,
## learn,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [28278] {data,
## featur,
## model,
## task,
## propos,
## learn} => {state} 0.1000000 0.7500000 5.625000 3
## [28279] {approach,
## method,
## show,
## dataset,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [28280] {approach,
## method,
## model,
## show,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [28281] {approach,
## method,
## model,
## dataset,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [28282] {approach,
## method,
## model,
## show,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28283] {method,
## model,
## show,
## dataset,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28284] {approach,
## model,
## show,
## dataset,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [28285] {approach,
## method,
## model,
## show,
## dataset,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [28286] {approach,
## evalu,
## featur,
## method,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [28287] {approach,
## evalu,
## method,
## network,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [28288] {approach,
## evalu,
## featur,
## method,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [28289] {evalu,
## featur,
## method,
## network,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [28290] {approach,
## evalu,
## featur,
## method,
## network,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [28291] {approach,
## evalu,
## featur,
## network,
## represent,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [28292] {approach,
## featur,
## method,
## network,
## represent,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [28293] {data,
## featur,
## task,
## dataset,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [28294] {data,
## network,
## task,
## dataset,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [28295] {data,
## featur,
## network,
## task,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [28296] {data,
## featur,
## network,
## task,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28297] {featur,
## network,
## task,
## dataset,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [28298] {data,
## featur,
## network,
## dataset,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [28299] {data,
## featur,
## network,
## task,
## dataset,
## learn} => {design} 0.1000000 1.0000000 7.500000 3
## [28300] {architectur,
## dataset,
## propos,
## addit,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [28301] {network,
## architectur,
## dataset,
## addit,
## effici,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [28302] {network,
## architectur,
## propos,
## addit,
## effici,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28303] {network,
## architectur,
## dataset,
## propos,
## addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [28304] {network,
## dataset,
## propos,
## addit,
## effici,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28305] {network,
## architectur,
## dataset,
## propos,
## effici,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [28306] {network,
## architectur,
## dataset,
## propos,
## addit,
## work} => {effici} 0.1000000 1.0000000 6.000000 3
## [28307] {approach,
## data,
## task,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28308] {approach,
## data,
## task,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28309] {approach,
## task,
## dataset,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28310] {approach,
## data,
## task,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28311] {data,
## task,
## dataset,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28312] {approach,
## data,
## dataset,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28313] {approach,
## data,
## task,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28314] {approach,
## data,
## task,
## dataset,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28315] {approach,
## data,
## represent,
## task,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28316] {approach,
## represent,
## task,
## dataset,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28317] {approach,
## data,
## represent,
## task,
## dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28318] {data,
## represent,
## task,
## dataset,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28319] {approach,
## data,
## represent,
## dataset,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28320] {approach,
## data,
## represent,
## task,
## dataset,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [28321] {approach,
## data,
## task,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28322] {approach,
## data,
## task,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28323] {approach,
## task,
## dataset,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28324] {approach,
## data,
## task,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28325] {data,
## task,
## dataset,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28326] {approach,
## data,
## dataset,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28327] {approach,
## data,
## task,
## dataset,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [28328] {approach,
## data,
## task,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28329] {approach,
## data,
## represent,
## task,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28330] {approach,
## represent,
## task,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28331] {approach,
## data,
## represent,
## task,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28332] {data,
## represent,
## task,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28333] {approach,
## data,
## represent,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28334] {approach,
## data,
## represent,
## task,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28335] {approach,
## data,
## task,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28336] {approach,
## data,
## task,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28337] {approach,
## task,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28338] {approach,
## data,
## task,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28339] {data,
## task,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28340] {approach,
## data,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28341] {approach,
## data,
## task,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28342] {approach,
## data,
## represent,
## task,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28343] {approach,
## data,
## task,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28344] {approach,
## represent,
## task,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28345] {approach,
## data,
## represent,
## task,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28346] {data,
## represent,
## task,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28347] {approach,
## data,
## represent,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28348] {approach,
## data,
## represent,
## task,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [28349] {approach,
## task,
## dataset,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28350] {approach,
## represent,
## task,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28351] {approach,
## represent,
## task,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28352] {approach,
## represent,
## task,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28353] {represent,
## task,
## dataset,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28354] {approach,
## represent,
## dataset,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28355] {approach,
## represent,
## task,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28356] {approach,
## task,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28357] {approach,
## task,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28358] {approach,
## task,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28359] {approach,
## task,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28360] {task,
## dataset,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28361] {approach,
## dataset,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28362] {approach,
## task,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28363] {approach,
## represent,
## task,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28364] {approach,
## task,
## dataset,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28365] {approach,
## represent,
## task,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28366] {approach,
## represent,
## task,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28367] {represent,
## task,
## dataset,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28368] {approach,
## represent,
## dataset,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28369] {approach,
## represent,
## task,
## dataset,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [28370] {approach,
## represent,
## task,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28371] {approach,
## task,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28372] {approach,
## represent,
## task,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28373] {approach,
## represent,
## task,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28374] {represent,
## task,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28375] {approach,
## represent,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28376] {approach,
## represent,
## task,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28377] {approach,
## data,
## task,
## dataset,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28378] {approach,
## data,
## represent,
## task,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28379] {approach,
## data,
## represent,
## task,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28380] {approach,
## represent,
## task,
## dataset,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28381] {data,
## represent,
## task,
## dataset,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28382] {approach,
## data,
## represent,
## dataset,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28383] {approach,
## data,
## represent,
## task,
## dataset,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28384] {approach,
## data,
## task,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28385] {approach,
## data,
## task,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28386] {approach,
## data,
## task,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28387] {approach,
## task,
## dataset,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28388] {data,
## task,
## dataset,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28389] {approach,
## data,
## dataset,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28390] {approach,
## data,
## task,
## dataset,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28391] {approach,
## data,
## represent,
## task,
## dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28392] {approach,
## data,
## task,
## dataset,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28393] {approach,
## data,
## represent,
## task,
## propos,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28394] {approach,
## represent,
## task,
## dataset,
## propos,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28395] {data,
## represent,
## task,
## dataset,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28396] {approach,
## data,
## represent,
## dataset,
## propos,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28397] {approach,
## data,
## represent,
## task,
## dataset,
## propos} => {semant} 0.1000000 1.0000000 6.000000 3
## [28398] {approach,
## data,
## represent,
## task,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28399] {approach,
## data,
## task,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28400] {approach,
## data,
## represent,
## task,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28401] {approach,
## represent,
## task,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28402] {data,
## represent,
## task,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28403] {approach,
## data,
## represent,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28404] {approach,
## data,
## represent,
## task,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28405] {approach,
## represent,
## task,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28406] {approach,
## task,
## dataset,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28407] {approach,
## represent,
## task,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28408] {approach,
## represent,
## task,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28409] {represent,
## task,
## dataset,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28410] {approach,
## represent,
## dataset,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28411] {approach,
## represent,
## task,
## dataset,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28412] {data,
## task,
## dataset,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28413] {data,
## represent,
## task,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28414] {data,
## represent,
## task,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28415] {represent,
## task,
## dataset,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28416] {data,
## represent,
## task,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28417] {data,
## represent,
## dataset,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28418] {data,
## represent,
## task,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28419] {data,
## task,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28420] {data,
## task,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28421] {data,
## task,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28422] {task,
## dataset,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28423] {data,
## task,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28424] {data,
## dataset,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28425] {data,
## task,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [28426] {data,
## represent,
## task,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28427] {data,
## task,
## dataset,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28428] {data,
## represent,
## task,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28429] {represent,
## task,
## dataset,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28430] {data,
## represent,
## task,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28431] {data,
## represent,
## dataset,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28432] {data,
## represent,
## task,
## dataset,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [28433] {data,
## represent,
## task,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28434] {data,
## task,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28435] {data,
## represent,
## task,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28436] {represent,
## task,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28437] {data,
## represent,
## task,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28438] {data,
## represent,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28439] {data,
## represent,
## task,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28440] {represent,
## task,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28441] {task,
## dataset,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28442] {represent,
## task,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28443] {represent,
## task,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28444] {represent,
## task,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28445] {represent,
## dataset,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28446] {represent,
## task,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28447] {data,
## represent,
## task,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28448] {data,
## task,
## dataset,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28449] {data,
## represent,
## task,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28450] {data,
## represent,
## task,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28451] {represent,
## task,
## dataset,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28452] {data,
## represent,
## dataset,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28453] {data,
## represent,
## task,
## dataset,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28454] {approach,
## data,
## dataset,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28455] {approach,
## data,
## represent,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28456] {approach,
## data,
## represent,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28457] {approach,
## represent,
## dataset,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28458] {approach,
## data,
## represent,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28459] {data,
## represent,
## dataset,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28460] {approach,
## data,
## represent,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28461] {approach,
## data,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28462] {approach,
## data,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28463] {approach,
## data,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28464] {approach,
## dataset,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28465] {approach,
## data,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28466] {data,
## dataset,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28467] {approach,
## data,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28468] {approach,
## data,
## represent,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28469] {approach,
## data,
## dataset,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28470] {approach,
## data,
## represent,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28471] {approach,
## represent,
## dataset,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28472] {approach,
## data,
## represent,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28473] {data,
## represent,
## dataset,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28474] {approach,
## data,
## represent,
## dataset,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [28475] {approach,
## data,
## represent,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28476] {approach,
## data,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28477] {approach,
## data,
## represent,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28478] {approach,
## represent,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28479] {approach,
## data,
## represent,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28480] {data,
## represent,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28481] {approach,
## data,
## represent,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28482] {approach,
## represent,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28483] {approach,
## dataset,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28484] {approach,
## represent,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28485] {approach,
## represent,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28486] {approach,
## represent,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28487] {represent,
## dataset,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28488] {approach,
## represent,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28489] {approach,
## data,
## represent,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28490] {approach,
## data,
## dataset,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28491] {approach,
## data,
## represent,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28492] {approach,
## data,
## represent,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28493] {approach,
## represent,
## dataset,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28494] {data,
## represent,
## dataset,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28495] {approach,
## data,
## represent,
## dataset,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28496] {data,
## represent,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28497] {data,
## dataset,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28498] {data,
## represent,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28499] {data,
## represent,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28500] {represent,
## dataset,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28501] {data,
## represent,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28502] {data,
## represent,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28503] {data,
## task,
## object,
## propos,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [28504] {data,
## featur,
## task,
## object,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [28505] {featur,
## task,
## object,
## propos,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [28506] {data,
## featur,
## object,
## propos,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [28507] {data,
## featur,
## task,
## propos,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [28508] {data,
## featur,
## task,
## object,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [28509] {data,
## featur,
## task,
## object,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [28510] {paper,
## represent,
## train,
## accuraci,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28511] {featur,
## paper,
## train,
## accuraci,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28512] {featur,
## paper,
## represent,
## train,
## accuraci,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [28513] {featur,
## paper,
## represent,
## accuraci,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [28514] {featur,
## represent,
## train,
## accuraci,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [28515] {featur,
## paper,
## represent,
## train,
## accuraci,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [28516] {featur,
## paper,
## represent,
## train,
## achiev,
## learn} => {accuraci} 0.1000000 1.0000000 5.000000 3
## [28517] {train,
## dataset,
## neural,
## result,
## specif,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [28518] {network,
## train,
## neural,
## result,
## specif,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28519] {network,
## dataset,
## neural,
## result,
## specif,
## potenti} => {train} 0.1000000 1.0000000 2.500000 3
## [28520] {network,
## train,
## dataset,
## result,
## specif,
## potenti} => {neural} 0.1000000 1.0000000 3.000000 3
## [28521] {network,
## train,
## dataset,
## neural,
## specif,
## potenti} => {result} 0.1000000 1.0000000 3.000000 3
## [28522] {network,
## train,
## dataset,
## neural,
## result,
## potenti} => {specif} 0.1000000 1.0000000 6.000000 3
## [28523] {network,
## train,
## dataset,
## neural,
## result,
## specif} => {potenti} 0.1000000 1.0000000 6.000000 3
## [28524] {method,
## appli,
## architectur,
## dataset,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28525] {method,
## network,
## appli,
## architectur,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [28526] {method,
## network,
## appli,
## architectur,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28527] {method,
## network,
## appli,
## architectur,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [28528] {network,
## appli,
## architectur,
## dataset,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [28529] {method,
## network,
## appli,
## dataset,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28530] {method,
## network,
## architectur,
## dataset,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [28531] {approach,
## show,
## appli,
## dataset,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28532] {approach,
## network,
## show,
## appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [28533] {approach,
## network,
## appli,
## dataset,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [28534] {approach,
## network,
## show,
## appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28535] {network,
## show,
## appli,
## dataset,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28536] {approach,
## network,
## show,
## appli,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [28537] {approach,
## network,
## show,
## dataset,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [28538] {data,
## represent,
## achiev,
## dataset,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [28539] {data,
## featur,
## achiev,
## dataset,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [28540] {data,
## featur,
## represent,
## achiev,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [28541] {data,
## featur,
## represent,
## achiev,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28542] {featur,
## represent,
## achiev,
## dataset,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [28543] {data,
## featur,
## represent,
## dataset,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [28544] {data,
## featur,
## represent,
## achiev,
## dataset,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [28545] {paper,
## task,
## train,
## learn,
## signific,
## effect} => {featur} 0.1000000 1.0000000 1.875000 3
## [28546] {featur,
## paper,
## task,
## train,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [28547] {featur,
## paper,
## task,
## learn,
## signific,
## effect} => {train} 0.1000000 1.0000000 2.500000 3
## [28548] {featur,
## paper,
## train,
## learn,
## signific,
## effect} => {task} 0.1000000 1.0000000 2.727273 3
## [28549] {featur,
## task,
## train,
## learn,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [28550] {featur,
## paper,
## task,
## train,
## learn,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [28551] {featur,
## paper,
## task,
## train,
## learn,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [28552] {approach,
## make,
## represent,
## perform,
## problem,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [28553] {approach,
## make,
## perform,
## propos,
## problem,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [28554] {approach,
## make,
## represent,
## propos,
## problem,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [28555] {make,
## represent,
## perform,
## propos,
## problem,
## applic} => {approach} 0.1000000 1.0000000 2.500000 3
## [28556] {approach,
## make,
## represent,
## perform,
## propos,
## applic} => {problem} 0.1000000 1.0000000 3.333333 3
## [28557] {approach,
## represent,
## perform,
## propos,
## problem,
## applic} => {make} 0.1000000 1.0000000 3.333333 3
## [28558] {approach,
## make,
## represent,
## perform,
## propos,
## problem} => {applic} 0.1000000 1.0000000 4.285714 3
## [28559] {classif,
## architectur,
## dataset,
## experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28560] {classif,
## network,
## architectur,
## dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [28561] {classif,
## network,
## architectur,
## experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28562] {network,
## architectur,
## dataset,
## experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [28563] {classif,
## network,
## dataset,
## experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28564] {classif,
## network,
## architectur,
## dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [28565] {classif,
## network,
## architectur,
## dataset,
## experi,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [28566] {algorithm,
## architectur,
## improv,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28567] {algorithm,
## architectur,
## dataset,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [28568] {architectur,
## dataset,
## improv,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [28569] {algorithm,
## architectur,
## dataset,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [28570] {algorithm,
## architectur,
## dataset,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [28571] {algorithm,
## dataset,
## improv,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28572] {algorithm,
## architectur,
## dataset,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [28573] {algorithm,
## architectur,
## improv,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [28574] {network,
## algorithm,
## architectur,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [28575] {network,
## architectur,
## improv,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [28576] {network,
## algorithm,
## architectur,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [28577] {network,
## algorithm,
## architectur,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [28578] {network,
## algorithm,
## improv,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28579] {network,
## algorithm,
## architectur,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [28580] {algorithm,
## architectur,
## dataset,
## improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [28581] {network,
## algorithm,
## architectur,
## improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28582] {network,
## architectur,
## dataset,
## improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [28583] {network,
## algorithm,
## architectur,
## dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [28584] {network,
## algorithm,
## architectur,
## dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [28585] {network,
## algorithm,
## dataset,
## improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28586] {network,
## algorithm,
## architectur,
## dataset,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [28587] {architectur,
## dataset,
## improv,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [28588] {network,
## architectur,
## improv,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28589] {network,
## architectur,
## dataset,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [28590] {network,
## architectur,
## dataset,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [28591] {network,
## architectur,
## dataset,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [28592] {network,
## dataset,
## improv,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28593] {network,
## architectur,
## dataset,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [28594] {algorithm,
## architectur,
## dataset,
## improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [28595] {network,
## algorithm,
## architectur,
## improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28596] {network,
## algorithm,
## architectur,
## dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [28597] {network,
## architectur,
## dataset,
## improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [28598] {network,
## algorithm,
## architectur,
## dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [28599] {network,
## algorithm,
## dataset,
## improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28600] {network,
## algorithm,
## architectur,
## dataset,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [28601] {algorithm,
## architectur,
## dataset,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [28602] {network,
## algorithm,
## architectur,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28603] {network,
## algorithm,
## architectur,
## dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [28604] {network,
## architectur,
## dataset,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [28605] {network,
## algorithm,
## architectur,
## dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [28606] {network,
## algorithm,
## dataset,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28607] {network,
## algorithm,
## architectur,
## dataset,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [28608] {algorithm,
## dataset,
## improv,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [28609] {network,
## algorithm,
## improv,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28610] {network,
## algorithm,
## dataset,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [28611] {network,
## dataset,
## improv,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [28612] {network,
## algorithm,
## dataset,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [28613] {network,
## algorithm,
## dataset,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [28614] {network,
## algorithm,
## dataset,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [28615] {data,
## featur,
## reduc,
## represent,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [28616] {data,
## network,
## reduc,
## represent,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [28617] {data,
## featur,
## network,
## reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [28618] {data,
## featur,
## network,
## reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [28619] {featur,
## network,
## reduc,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [28620] {data,
## featur,
## network,
## reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [28621] {data,
## featur,
## network,
## represent,
## show,
## task} => {reduc} 0.1000000 1.0000000 4.285714 3
## [28622] {approach,
## classif,
## method,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [28623] {classif,
## method,
## architectur,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28624] {approach,
## classif,
## architectur,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [28625] {approach,
## classif,
## method,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [28626] {approach,
## method,
## architectur,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [28627] {approach,
## classif,
## method,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28628] {approach,
## classif,
## method,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [28629] {approach,
## classif,
## method,
## architectur,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [28630] {classif,
## method,
## network,
## architectur,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [28631] {approach,
## classif,
## network,
## architectur,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [28632] {approach,
## classif,
## method,
## network,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [28633] {approach,
## method,
## network,
## architectur,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [28634] {approach,
## classif,
## method,
## network,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28635] {approach,
## classif,
## method,
## network,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [28636] {classif,
## method,
## architectur,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28637] {classif,
## method,
## network,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [28638] {classif,
## network,
## architectur,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [28639] {classif,
## method,
## network,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [28640] {method,
## network,
## architectur,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [28641] {classif,
## method,
## network,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28642] {classif,
## method,
## network,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [28643] {approach,
## classif,
## architectur,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28644] {approach,
## classif,
## network,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [28645] {classif,
## network,
## architectur,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28646] {approach,
## classif,
## network,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [28647] {approach,
## network,
## architectur,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [28648] {approach,
## classif,
## network,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28649] {approach,
## classif,
## network,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [28650] {approach,
## classif,
## method,
## architectur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28651] {approach,
## classif,
## method,
## network,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [28652] {classif,
## method,
## network,
## architectur,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28653] {approach,
## classif,
## network,
## architectur,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [28654] {approach,
## method,
## network,
## architectur,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [28655] {approach,
## classif,
## method,
## network,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28656] {approach,
## classif,
## method,
## network,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [28657] {approach,
## method,
## architectur,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28658] {approach,
## method,
## network,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [28659] {method,
## network,
## architectur,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28660] {approach,
## network,
## architectur,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [28661] {approach,
## method,
## network,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [28662] {approach,
## method,
## network,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28663] {approach,
## method,
## network,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [28664] {approach,
## classif,
## method,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28665] {approach,
## classif,
## method,
## network,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [28666] {classif,
## method,
## network,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28667] {approach,
## classif,
## network,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [28668] {approach,
## classif,
## method,
## network,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [28669] {approach,
## method,
## network,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [28670] {approach,
## classif,
## method,
## network,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [28671] {approach,
## method,
## show,
## dataset,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [28672] {approach,
## method,
## dataset,
## experi,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [28673] {approach,
## method,
## show,
## experi,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28674] {approach,
## method,
## show,
## dataset,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [28675] {method,
## show,
## dataset,
## experi,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28676] {approach,
## show,
## dataset,
## experi,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [28677] {approach,
## method,
## show,
## dataset,
## perform,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [28678] {data,
## model,
## task,
## object,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28679] {data,
## featur,
## task,
## object,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28680] {data,
## featur,
## model,
## task,
## object,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28681] {data,
## featur,
## model,
## task,
## object,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [28682] {featur,
## model,
## task,
## object,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28683] {data,
## featur,
## model,
## object,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28684] {data,
## featur,
## model,
## task,
## propos,
## learn} => {object} 0.1000000 0.7500000 2.812500 3
## [28685] {approach,
## classif,
## method,
## architectur,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28686] {approach,
## classif,
## method,
## network,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [28687] {classif,
## method,
## network,
## architectur,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28688] {approach,
## classif,
## network,
## architectur,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [28689] {approach,
## classif,
## method,
## network,
## architectur,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [28690] {approach,
## method,
## network,
## architectur,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [28691] {approach,
## classif,
## method,
## network,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28692] {algorithm,
## architectur,
## dataset,
## improv,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [28693] {network,
## algorithm,
## architectur,
## improv,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28694] {network,
## algorithm,
## architectur,
## dataset,
## improv,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [28695] {network,
## architectur,
## dataset,
## improv,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [28696] {network,
## algorithm,
## architectur,
## dataset,
## improv,
## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [28697] {network,
## algorithm,
## architectur,
## dataset,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [28698] {network,
## algorithm,
## dataset,
## improv,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28699] {approach,
## make,
## dataset,
## perform,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28700] {approach,
## make,
## model,
## dataset,
## perform,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [28701] {approach,
## make,
## model,
## perform,
## problem,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28702] {approach,
## make,
## model,
## dataset,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [28703] {make,
## model,
## dataset,
## perform,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [28704] {approach,
## make,
## model,
## dataset,
## perform,
## learn} => {problem} 0.1000000 1.0000000 3.333333 3
## [28705] {approach,
## model,
## dataset,
## perform,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [28706] {make,
## represent,
## dataset,
## perform,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28707] {make,
## model,
## represent,
## dataset,
## perform,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28708] {make,
## model,
## dataset,
## perform,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28709] {make,
## model,
## represent,
## dataset,
## perform,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [28710] {make,
## model,
## represent,
## perform,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28711] {make,
## model,
## represent,
## dataset,
## propos,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [28712] {model,
## represent,
## dataset,
## perform,
## propos,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [28713] {approach,
## classif,
## method,
## dataset,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [28714] {approach,
## classif,
## method,
## show,
## improv,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28715] {approach,
## classif,
## method,
## show,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [28716] {classif,
## method,
## show,
## dataset,
## improv,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [28717] {approach,
## classif,
## show,
## dataset,
## improv,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [28718] {approach,
## classif,
## method,
## show,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [28719] {approach,
## method,
## show,
## dataset,
## improv,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [28720] {data,
## represent,
## task,
## recognit,
## result,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [28721] {data,
## show,
## task,
## recognit,
## result,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28722] {data,
## represent,
## show,
## task,
## recognit,
## result} => {learn} 0.1000000 1.0000000 2.307692 3
## [28723] {represent,
## show,
## task,
## recognit,
## result,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28724] {data,
## represent,
## show,
## recognit,
## result,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28725] {data,
## represent,
## show,
## task,
## recognit,
## learn} => {result} 0.1000000 1.0000000 3.000000 3
## [28726] {data,
## represent,
## show,
## task,
## result,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [28727] {approach,
## algorithm,
## dataset,
## improv,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28728] {approach,
## network,
## algorithm,
## dataset,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [28729] {approach,
## network,
## algorithm,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28730] {network,
## algorithm,
## dataset,
## improv,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28731] {approach,
## network,
## dataset,
## improv,
## neural,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [28732] {approach,
## network,
## algorithm,
## dataset,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [28733] {approach,
## network,
## algorithm,
## dataset,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [28734] {approach,
## data,
## task,
## dataset,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28735] {approach,
## data,
## represent,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28736] {approach,
## data,
## represent,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28737] {approach,
## represent,
## task,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28738] {approach,
## data,
## represent,
## task,
## dataset,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28739] {data,
## represent,
## task,
## dataset,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [28740] {approach,
## data,
## represent,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28741] {approach,
## data,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28742] {approach,
## data,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28743] {approach,
## data,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28744] {approach,
## task,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28745] {approach,
## data,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28746] {data,
## task,
## dataset,
## propos,
## work,
## learn} => {approach} 0.1000000 0.7500000 1.875000 3
## [28747] {approach,
## data,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28748] {approach,
## data,
## represent,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [28749] {approach,
## data,
## task,
## dataset,
## propos,
## work} => {represent} 0.1000000 1.0000000 2.000000 3
## [28750] {approach,
## data,
## represent,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28751] {approach,
## represent,
## task,
## dataset,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [28752] {approach,
## data,
## represent,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [28753] {data,
## represent,
## task,
## dataset,
## propos,
## work} => {approach} 0.1000000 1.0000000 2.500000 3
## [28754] {approach,
## data,
## represent,
## dataset,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [28755] {approach,
## data,
## represent,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28756] {approach,
## data,
## task,
## propos,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28757] {approach,
## data,
## represent,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28758] {approach,
## represent,
## task,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28759] {approach,
## data,
## represent,
## task,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28760] {data,
## represent,
## task,
## propos,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [28761] {approach,
## data,
## represent,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28762] {approach,
## represent,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28763] {approach,
## task,
## dataset,
## propos,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28764] {approach,
## represent,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28765] {approach,
## represent,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28766] {approach,
## represent,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28767] {represent,
## task,
## dataset,
## propos,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [28768] {approach,
## represent,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28769] {approach,
## data,
## represent,
## task,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28770] {approach,
## data,
## task,
## dataset,
## propos,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28771] {approach,
## data,
## represent,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [28772] {approach,
## data,
## represent,
## task,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28773] {approach,
## represent,
## task,
## dataset,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28774] {data,
## represent,
## task,
## dataset,
## propos,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [28775] {approach,
## data,
## represent,
## dataset,
## propos,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28776] {data,
## represent,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28777] {data,
## task,
## dataset,
## propos,
## work,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [28778] {data,
## represent,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28779] {data,
## represent,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28780] {represent,
## task,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28781] {data,
## represent,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28782] {data,
## represent,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28783] {data,
## task,
## dataset,
## propos,
## work,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [28784] {data,
## model,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28785] {data,
## model,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28786] {data,
## model,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28787] {model,
## task,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28788] {data,
## model,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28789] {data,
## model,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28790] {data,
## task,
## dataset,
## propos,
## work,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [28791] {data,
## featur,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28792] {data,
## featur,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28793] {data,
## featur,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28794] {featur,
## task,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28795] {data,
## featur,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28796] {data,
## featur,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28797] {data,
## task,
## dataset,
## propos,
## work,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [28798] {data,
## network,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28799] {data,
## network,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28800] {data,
## network,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28801] {network,
## task,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28802] {data,
## network,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28803] {data,
## network,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28804] {data,
## model,
## task,
## dataset,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28805] {data,
## featur,
## task,
## dataset,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28806] {data,
## featur,
## model,
## task,
## dataset,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28807] {data,
## featur,
## model,
## task,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28808] {featur,
## model,
## task,
## dataset,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28809] {data,
## featur,
## model,
## task,
## dataset,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28810] {data,
## featur,
## model,
## dataset,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28811] {data,
## model,
## task,
## dataset,
## propos,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [28812] {data,
## featur,
## task,
## dataset,
## propos,
## work} => {model} 0.1000000 1.0000000 1.875000 3
## [28813] {data,
## featur,
## model,
## task,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [28814] {data,
## featur,
## model,
## task,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28815] {featur,
## model,
## task,
## dataset,
## propos,
## work} => {data} 0.1000000 1.0000000 2.307692 3
## [28816] {data,
## featur,
## model,
## task,
## dataset,
## propos} => {work} 0.1000000 1.0000000 2.500000 3
## [28817] {data,
## featur,
## model,
## dataset,
## propos,
## work} => {task} 0.1000000 1.0000000 2.727273 3
## [28818] {data,
## model,
## task,
## propos,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28819] {data,
## featur,
## task,
## propos,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28820] {data,
## featur,
## model,
## task,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28821] {data,
## featur,
## model,
## task,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28822] {featur,
## model,
## task,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28823] {data,
## featur,
## model,
## task,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [28824] {data,
## featur,
## model,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28825] {model,
## task,
## dataset,
## propos,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28826] {featur,
## task,
## dataset,
## propos,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28827] {featur,
## model,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28828] {featur,
## model,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28829] {featur,
## model,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28830] {featur,
## model,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28831] {featur,
## model,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28832] {data,
## represent,
## show,
## task,
## dataset,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [28833] {data,
## network,
## represent,
## task,
## dataset,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [28834] {data,
## network,
## show,
## task,
## dataset,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28835] {data,
## network,
## represent,
## show,
## task,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [28836] {data,
## network,
## represent,
## show,
## task,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28837] {network,
## represent,
## show,
## task,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28838] {data,
## network,
## represent,
## show,
## dataset,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28839] {data,
## model,
## task,
## dataset,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28840] {data,
## featur,
## task,
## dataset,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28841] {data,
## featur,
## model,
## task,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28842] {data,
## featur,
## model,
## task,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [28843] {data,
## featur,
## model,
## task,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [28844] {featur,
## model,
## task,
## dataset,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28845] {data,
## featur,
## model,
## dataset,
## propos,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [28846] {approach,
## data,
## represent,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28847] {approach,
## data,
## dataset,
## propos,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28848] {approach,
## data,
## represent,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28849] {approach,
## data,
## represent,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28850] {approach,
## represent,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28851] {approach,
## data,
## represent,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28852] {data,
## represent,
## dataset,
## propos,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [28853] {data,
## model,
## dataset,
## propos,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28854] {data,
## featur,
## dataset,
## propos,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28855] {data,
## featur,
## model,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28856] {data,
## featur,
## model,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28857] {data,
## featur,
## model,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28858] {featur,
## model,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28859] {data,
## featur,
## model,
## dataset,
## propos,
## learn} => {work} 0.1000000 0.7500000 1.875000 3
## [28860] {data,
## model,
## represent,
## dataset,
## propos,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28861] {data,
## featur,
## represent,
## dataset,
## propos,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28862] {data,
## featur,
## model,
## represent,
## dataset,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28863] {data,
## featur,
## model,
## dataset,
## propos,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [28864] {data,
## featur,
## model,
## represent,
## dataset,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [28865] {data,
## featur,
## model,
## represent,
## propos,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28866] {featur,
## model,
## represent,
## dataset,
## propos,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28867] {data,
## featur,
## task,
## dataset,
## learn,
## care,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [28868] {data,
## network,
## task,
## dataset,
## learn,
## care,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [28869] {data,
## featur,
## network,
## task,
## dataset,
## care,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [28870] {data,
## featur,
## network,
## task,
## learn,
## care,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28871] {featur,
## network,
## task,
## dataset,
## learn,
## care,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [28872] {data,
## featur,
## network,
## dataset,
## learn,
## care,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [28873] {data,
## featur,
## network,
## task,
## dataset,
## learn,
## care} => {design} 0.1000000 1.0000000 7.500000 3
## [28874] {data,
## featur,
## network,
## task,
## dataset,
## learn,
## design} => {care} 0.1000000 1.0000000 10.000000 3
## [28875] {boltzmann,
## data,
## machin,
## model,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [28876] {boltzmann,
## data,
## featur,
## machin,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [28877] {boltzmann,
## data,
## featur,
## machin,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [28878] {boltzmann,
## featur,
## machin,
## model,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [28879] {boltzmann,
## data,
## featur,
## machin,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [28880] {boltzmann,
## data,
## featur,
## model,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [28881] {boltzmann,
## data,
## featur,
## machin,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [28882] {data,
## featur,
## machin,
## model,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [28883] {data,
## model,
## task,
## propos,
## learn,
## art,
## state} => {featur} 0.1000000 1.0000000 1.875000 3
## [28884] {data,
## featur,
## task,
## propos,
## learn,
## art,
## state} => {model} 0.1000000 1.0000000 1.875000 3
## [28885] {data,
## featur,
## model,
## task,
## learn,
## art,
## state} => {propos} 0.1000000 1.0000000 2.000000 3
## [28886] {data,
## featur,
## model,
## task,
## propos,
## art,
## state} => {learn} 0.1000000 1.0000000 2.307692 3
## [28887] {featur,
## model,
## task,
## propos,
## learn,
## art,
## state} => {data} 0.1000000 1.0000000 2.307692 3
## [28888] {data,
## featur,
## model,
## propos,
## learn,
## art,
## state} => {task} 0.1000000 1.0000000 2.727273 3
## [28889] {data,
## featur,
## model,
## task,
## propos,
## learn,
## art} => {state} 0.1000000 1.0000000 7.500000 3
## [28890] {data,
## featur,
## model,
## task,
## propos,
## learn,
## state} => {art} 0.1000000 1.0000000 7.500000 3
## [28891] {approach,
## data,
## task,
## dataset,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28892] {approach,
## data,
## represent,
## task,
## dataset,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28893] {approach,
## data,
## represent,
## task,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28894] {approach,
## represent,
## task,
## dataset,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28895] {approach,
## data,
## represent,
## task,
## dataset,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28896] {data,
## represent,
## task,
## dataset,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28897] {approach,
## data,
## represent,
## dataset,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28898] {approach,
## data,
## represent,
## task,
## dataset,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28899] {approach,
## data,
## task,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28900] {approach,
## data,
## task,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28901] {approach,
## data,
## task,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28902] {approach,
## task,
## dataset,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28903] {approach,
## data,
## task,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28904] {data,
## task,
## dataset,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28905] {approach,
## data,
## dataset,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28906] {approach,
## data,
## task,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28907] {approach,
## data,
## represent,
## task,
## dataset,
## work,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28908] {approach,
## data,
## task,
## dataset,
## propos,
## work,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28909] {approach,
## data,
## represent,
## task,
## propos,
## work,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28910] {approach,
## represent,
## task,
## dataset,
## propos,
## work,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28911] {approach,
## data,
## represent,
## task,
## dataset,
## propos,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28912] {data,
## represent,
## task,
## dataset,
## propos,
## work,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28913] {approach,
## data,
## represent,
## dataset,
## propos,
## work,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28914] {approach,
## data,
## represent,
## task,
## dataset,
## propos,
## work} => {semant} 0.1000000 1.0000000 6.000000 3
## [28915] {approach,
## data,
## represent,
## task,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28916] {approach,
## data,
## task,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28917] {approach,
## data,
## represent,
## task,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28918] {approach,
## represent,
## task,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28919] {approach,
## data,
## represent,
## task,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28920] {data,
## represent,
## task,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28921] {approach,
## data,
## represent,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28922] {approach,
## data,
## represent,
## task,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28923] {approach,
## represent,
## task,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28924] {approach,
## task,
## dataset,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28925] {approach,
## represent,
## task,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28926] {approach,
## represent,
## task,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28927] {approach,
## represent,
## task,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28928] {represent,
## task,
## dataset,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28929] {approach,
## represent,
## dataset,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28930] {approach,
## represent,
## task,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28931] {approach,
## data,
## represent,
## task,
## dataset,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28932] {approach,
## data,
## task,
## dataset,
## propos,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28933] {approach,
## data,
## represent,
## task,
## dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28934] {approach,
## data,
## represent,
## task,
## propos,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28935] {approach,
## represent,
## task,
## dataset,
## propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28936] {data,
## represent,
## task,
## dataset,
## propos,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28937] {approach,
## data,
## represent,
## dataset,
## propos,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28938] {approach,
## data,
## represent,
## task,
## dataset,
## propos,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28939] {data,
## represent,
## task,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28940] {data,
## task,
## dataset,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28941] {data,
## represent,
## task,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28942] {data,
## represent,
## task,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28943] {represent,
## task,
## dataset,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28944] {data,
## represent,
## task,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28945] {data,
## represent,
## dataset,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28946] {data,
## represent,
## task,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28947] {approach,
## data,
## represent,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28948] {approach,
## data,
## dataset,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28949] {approach,
## data,
## represent,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28950] {approach,
## data,
## represent,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28951] {approach,
## represent,
## dataset,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28952] {approach,
## data,
## represent,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28953] {data,
## represent,
## dataset,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28954] {approach,
## data,
## represent,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28955] {algorithm,
## architectur,
## dataset,
## improv,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [28956] {network,
## algorithm,
## architectur,
## improv,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28957] {network,
## algorithm,
## architectur,
## dataset,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [28958] {network,
## architectur,
## dataset,
## improv,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [28959] {network,
## algorithm,
## architectur,
## dataset,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [28960] {network,
## algorithm,
## architectur,
## dataset,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [28961] {network,
## algorithm,
## dataset,
## improv,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28962] {network,
## algorithm,
## architectur,
## dataset,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [28963] {approach,
## classif,
## method,
## architectur,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28964] {approach,
## classif,
## method,
## network,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [28965] {classif,
## method,
## network,
## architectur,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28966] {approach,
## classif,
## network,
## architectur,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [28967] {approach,
## classif,
## method,
## network,
## architectur,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [28968] {approach,
## method,
## network,
## architectur,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [28969] {approach,
## classif,
## method,
## network,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28970] {approach,
## classif,
## method,
## network,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [28971] {approach,
## data,
## represent,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28972] {approach,
## data,
## task,
## dataset,
## propos,
## work,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [28973] {approach,
## data,
## represent,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28974] {approach,
## data,
## represent,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28975] {approach,
## represent,
## task,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28976] {approach,
## data,
## represent,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28977] {data,
## represent,
## task,
## dataset,
## propos,
## work,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [28978] {approach,
## data,
## represent,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28979] {data,
## model,
## task,
## dataset,
## propos,
## work,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [28980] {data,
## featur,
## task,
## dataset,
## propos,
## work,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [28981] {data,
## featur,
## model,
## task,
## dataset,
## work,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [28982] {data,
## featur,
## model,
## task,
## dataset,
## propos,
## work} => {learn} 0.1000000 1.0000000 2.307692 3
## [28983] {data,
## featur,
## model,
## task,
## propos,
## work,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28984] {featur,
## model,
## task,
## dataset,
## propos,
## work,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [28985] {data,
## featur,
## model,
## task,
## dataset,
## propos,
## learn} => {work} 0.1000000 1.0000000 2.500000 3
## [28986] {data,
## featur,
## model,
## dataset,
## propos,
## work,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [28987] {approach,
## data,
## represent,
## task,
## dataset,
## work,
## learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28988] {approach,
## data,
## task,
## dataset,
## propos,
## work,
## learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28989] {approach,
## data,
## represent,
## task,
## dataset,
## propos,
## work,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28990] {approach,
## data,
## represent,
## task,
## propos,
## work,
## learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28991] {approach,
## represent,
## task,
## dataset,
## propos,
## work,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28992] {approach,
## data,
## represent,
## task,
## dataset,
## propos,
## learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28993] {data,
## represent,
## task,
## dataset,
## propos,
## work,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28994] {approach,
## data,
## represent,
## dataset,
## propos,
## work,
## learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28995] {approach,
## data,
## represent,
## task,
## dataset,
## propos,
## work,
## learn} => {semant} 0.1000000 1.0000000 6.000000 3## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 10
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[1891 item(s), 102 transaction(s)] done [0.00s].
## sorting and recoding items ... [121 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 done [0.00s].
## writing ... [781 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
## lhs rhs support confidence
## [1] {experiment} => {result} 0.1078431 0.8461538
## [2] {practic} => {network} 0.1078431 0.9166667
## [3] {robust} => {network} 0.1176471 1.0000000
## [4] {optim} => {network} 0.1078431 0.9166667
## [5] {find} => {perform} 0.1176471 0.9230769
## [6] {util} => {network} 0.1078431 0.9166667
## [7] {order} => {network} 0.1176471 0.9230769
## [8] {cnn} => {convolut} 0.1078431 0.8461538
## [9] {cnn} => {neural} 0.1176471 0.9230769
## [10] {cnn} => {network} 0.1176471 0.9230769
## [11] {general} => {network} 0.1078431 0.7333333
## [12] {design} => {network} 0.1274510 0.8666667
## [13] {specif} => {paper} 0.1078431 0.7333333
## [14] {specif} => {network} 0.1078431 0.7333333
## [15] {natur} => {neural} 0.1078431 0.7857143
## [16] {natur} => {network} 0.1176471 0.8571429
## [17] {potenti} => {perform} 0.1078431 0.7333333
## [18] {potenti} => {network} 0.1078431 0.7333333
## [19] {exist} => {method} 0.1078431 0.7857143
## [20] {paramet} => {train} 0.1176471 0.8571429
## [21] {paramet} => {perform} 0.1078431 0.7857143
## [22] {addit} => {neural} 0.1176471 0.8000000
## [23] {addit} => {network} 0.1274510 0.8666667
## [24] {introduc} => {show} 0.1176471 0.7058824
## [25] {introduc} => {train} 0.1176471 0.7058824
## [26] {introduc} => {neural} 0.1274510 0.7647059
## [27] {introduc} => {network} 0.1372549 0.8235294
## [28] {generat} => {model} 0.1176471 0.8000000
## [29] {generat} => {neural} 0.1078431 0.7333333
## [30] {experi} => {paper} 0.1176471 0.7058824
## [31] {experi} => {perform} 0.1176471 0.7058824
## [32] {experi} => {network} 0.1372549 0.8235294
## [33] {represent} => {model} 0.1274510 0.7647059
## [34] {represent} => {neural} 0.1176471 0.7058824
## [35] {input} => {neural} 0.1176471 0.7500000
## [36] {input} => {network} 0.1470588 0.9375000
## [37] {extract} => {featur} 0.1372549 0.7777778
## [38] {extract} => {neural} 0.1470588 0.8333333
## [39] {extract} => {network} 0.1568627 0.8888889
## [40] {signific} => {train} 0.1176471 0.7058824
## [41] {signific} => {perform} 0.1176471 0.7058824
## [42] {visual} => {base} 0.1176471 0.7058824
## [43] {visual} => {model} 0.1372549 0.8235294
## [44] {visual} => {neural} 0.1274510 0.7647059
## [45] {visual} => {network} 0.1666667 1.0000000
## [46] {high} => {network} 0.1470588 0.8333333
## [47] {provid} => {model} 0.1274510 0.7647059
## [48] {provid} => {network} 0.1176471 0.7058824
## [49] {challeng} => {network} 0.1470588 0.7894737
## [50] {layer} => {show} 0.1176471 0.7058824
## [51] {layer} => {neural} 0.1470588 0.8823529
## [52] {layer} => {network} 0.1372549 0.8235294
## [53] {classifi} => {classif} 0.1372549 0.7000000
## [54] {classifi} => {neural} 0.1568627 0.8000000
## [55] {classifi} => {network} 0.1862745 0.9500000
## [56] {time} => {network} 0.1470588 0.7894737
## [57] {predict} => {network} 0.1568627 0.7619048
## [58] {recent} => {network} 0.1470588 0.7894737
## [59] {combin} => {neural} 0.1666667 0.8095238
## [60] {combin} => {network} 0.1862745 0.9047619
## [61] {problem} => {paper} 0.1764706 0.8571429
## [62] {problem} => {neural} 0.1470588 0.7142857
## [63] {problem} => {network} 0.1666667 0.8095238
## [64] {recognit} => {model} 0.1372549 0.7368421
## [65] {recognit} => {network} 0.1568627 0.8421053
## [66] {appli} => {paper} 0.1470588 0.7142857
## [67] {learn} => {model} 0.1372549 0.7000000
## [68] {learn} => {network} 0.1568627 0.8000000
## [69] {develop} => {comput} 0.1372549 0.7000000
## [70] {develop} => {network} 0.1568627 0.8000000
## [71] {system} => {network} 0.1666667 0.7727273
## [72] {set} => {network} 0.1862745 0.8260870
## [73] {effici} => {network} 0.1666667 0.7391304
## [74] {object} => {network} 0.1666667 0.8095238
## [75] {larg} => {network} 0.2156863 0.9565217
## [76] {compar} => {perform} 0.1666667 0.7083333
## [77] {framework} => {network} 0.1960784 0.7692308
## [78] {classif} => {network} 0.2058824 0.7500000
## [79] {inform} => {network} 0.1862745 0.7307692
## [80] {machin} => {network} 0.2058824 0.7777778
## [81] {work} => {network} 0.1960784 0.7407407
## [82] {present} => {neural} 0.2058824 0.7241379
## [83] {present} => {network} 0.2352941 0.8275862
## [84] {accuraci} => {network} 0.2254902 0.7931034
## [85] {dataset} => {train} 0.2254902 0.7419355
## [86] {dataset} => {network} 0.2254902 0.7419355
## [87] {architectur} => {network} 0.2450980 0.8064516
## [88] {improv} => {network} 0.2254902 0.7187500
## [89] {imag} => {network} 0.2647059 0.8709677
## [90] {achiev} => {network} 0.2745098 0.8484848
## [91] {task} => {network} 0.2352941 0.7272727
## [92] {base} => {network} 0.2745098 0.8484848
## [93] {featur} => {network} 0.2745098 0.8235294
## [94] {approach} => {network} 0.2647059 0.7500000
## [95] {algorithm} => {network} 0.2549020 0.7027027
## [96] {comput} => {network} 0.2941176 0.8108108
## [97] {propos} => {network} 0.2843137 0.7435897
## [98] {data} => {network} 0.2843137 0.7435897
## [99] {convolut} => {network} 0.3725490 0.9743590
## [100] {show} => {network} 0.3725490 0.7916667
## [101] {train} => {network} 0.3921569 0.7843137
## [102] {perform} => {network} 0.3627451 0.7115385
## [103] {model} => {network} 0.3921569 0.7407407
## [104] {neural} => {network} 0.5882353 0.9677419
## [105] {network} => {neural} 0.5882353 0.8000000
## [106] {cnn,convolut} => {neural} 0.1078431 1.0000000
## [107] {cnn,neural} => {convolut} 0.1078431 0.9166667
## [108] {cnn,convolut} => {network} 0.1078431 1.0000000
## [109] {cnn,network} => {convolut} 0.1078431 0.9166667
## [110] {cnn,neural} => {network} 0.1176471 1.0000000
## [111] {cnn,network} => {neural} 0.1176471 1.0000000
## [112] {natur,neural} => {network} 0.1078431 1.0000000
## [113] {natur,network} => {neural} 0.1078431 0.9166667
## [114] {neural,addit} => {network} 0.1176471 1.0000000
## [115] {network,addit} => {neural} 0.1176471 0.9230769
## [116] {neural,introduc} => {network} 0.1176471 0.9230769
## [117] {network,introduc} => {neural} 0.1176471 0.8571429
## [118] {neural,experi} => {network} 0.1078431 1.0000000
## [119] {network,experi} => {neural} 0.1078431 0.7857143
## [120] {input,neural} => {network} 0.1176471 1.0000000
## [121] {input,network} => {neural} 0.1176471 0.8000000
## [122] {extract,featur} => {neural} 0.1078431 0.7857143
## [123] {neural,extract} => {featur} 0.1078431 0.7333333
## [124] {extract,featur} => {network} 0.1274510 0.9285714
## [125] {network,extract} => {featur} 0.1274510 0.8125000
## [126] {model,extract} => {neural} 0.1078431 0.9166667
## [127] {neural,extract} => {model} 0.1078431 0.7333333
## [128] {neural,extract} => {network} 0.1372549 0.9333333
## [129] {network,extract} => {neural} 0.1372549 0.8750000
## [130] {base,visual} => {network} 0.1176471 1.0000000
## [131] {network,visual} => {base} 0.1176471 0.7058824
## [132] {model,visual} => {neural} 0.1176471 0.8571429
## [133] {neural,visual} => {model} 0.1176471 0.9230769
## [134] {model,visual} => {network} 0.1372549 1.0000000
## [135] {network,visual} => {model} 0.1372549 0.8235294
## [136] {neural,visual} => {network} 0.1274510 1.0000000
## [137] {network,visual} => {neural} 0.1274510 0.7647059
## [138] {neural,high} => {network} 0.1078431 1.0000000
## [139] {network,high} => {neural} 0.1078431 0.7333333
## [140] {neural,challeng} => {network} 0.1274510 1.0000000
## [141] {network,challeng} => {neural} 0.1274510 0.8666667
## [142] {layer,neural} => {network} 0.1372549 0.9333333
## [143] {layer,network} => {neural} 0.1372549 1.0000000
## [144] {classif,classifi} => {neural} 0.1078431 0.7857143
## [145] {classif,classifi} => {network} 0.1274510 0.9285714
## [146] {achiev,classifi} => {network} 0.1078431 1.0000000
## [147] {featur,classifi} => {neural} 0.1078431 0.9166667
## [148] {featur,classifi} => {network} 0.1176471 1.0000000
## [149] {approach,classifi} => {neural} 0.1078431 0.8461538
## [150] {approach,classifi} => {network} 0.1274510 1.0000000
## [151] {convolut,classifi} => {network} 0.1176471 1.0000000
## [152] {neural,classifi} => {network} 0.1568627 1.0000000
## [153] {network,classifi} => {neural} 0.1568627 0.8421053
## [154] {convolut,time} => {network} 0.1078431 1.0000000
## [155] {network,time} => {convolut} 0.1078431 0.7333333
## [156] {neural,time} => {network} 0.1078431 1.0000000
## [157] {network,time} => {neural} 0.1078431 0.7333333
## [158] {neural,predict} => {network} 0.1176471 1.0000000
## [159] {network,predict} => {neural} 0.1176471 0.7500000
## [160] {recent,show} => {network} 0.1078431 0.9166667
## [161] {network,recent} => {show} 0.1078431 0.7333333
## [162] {model,recent} => {network} 0.1078431 0.8461538
## [163] {network,recent} => {model} 0.1078431 0.7333333
## [164] {neural,recent} => {network} 0.1176471 1.0000000
## [165] {network,recent} => {neural} 0.1176471 0.8000000
## [166] {combin,convolut} => {network} 0.1274510 1.0000000
## [167] {combin,result} => {neural} 0.1078431 0.9166667
## [168] {combin,paper} => {network} 0.1078431 0.9166667
## [169] {combin,neural} => {network} 0.1568627 0.9411765
## [170] {combin,network} => {neural} 0.1568627 0.8421053
## [171] {problem,result} => {paper} 0.1078431 0.9166667
## [172] {show,problem} => {paper} 0.1078431 0.9166667
## [173] {model,problem} => {paper} 0.1078431 1.0000000
## [174] {neural,problem} => {paper} 0.1176471 0.8000000
## [175] {paper,problem} => {network} 0.1372549 0.7777778
## [176] {network,problem} => {paper} 0.1372549 0.8235294
## [177] {neural,problem} => {network} 0.1470588 1.0000000
## [178] {network,problem} => {neural} 0.1470588 0.8823529
## [179] {propos,recognit} => {network} 0.1078431 0.9166667
## [180] {model,recognit} => {network} 0.1078431 0.7857143
## [181] {neural,recognit} => {network} 0.1176471 1.0000000
## [182] {network,recognit} => {neural} 0.1176471 0.7500000
## [183] {neural,appli} => {network} 0.1176471 1.0000000
## [184] {network,appli} => {neural} 0.1176471 0.8571429
## [185] {train,learn} => {network} 0.1078431 0.8461538
## [186] {model,learn} => {network} 0.1176471 0.8571429
## [187] {network,learn} => {model} 0.1176471 0.7500000
## [188] {neural,learn} => {network} 0.1274510 1.0000000
## [189] {network,learn} => {neural} 0.1274510 0.8125000
## [190] {comput,develop} => {network} 0.1176471 0.8571429
## [191] {network,develop} => {comput} 0.1176471 0.7500000
## [192] {model,develop} => {network} 0.1078431 0.9166667
## [193] {neural,develop} => {network} 0.1078431 0.9166667
## [194] {dataset,stateoftheart} => {train} 0.1078431 0.8461538
## [195] {train,stateoftheart} => {dataset} 0.1078431 0.7333333
## [196] {propos,stateoftheart} => {train} 0.1078431 0.7333333
## [197] {train,stateoftheart} => {propos} 0.1078431 0.7333333
## [198] {convolut,stateoftheart} => {network} 0.1078431 1.0000000
## [199] {neural,stateoftheart} => {network} 0.1078431 1.0000000
## [200] {convolut,system} => {network} 0.1078431 1.0000000
## [201] {neural,system} => {network} 0.1470588 1.0000000
## [202] {network,system} => {neural} 0.1470588 0.8823529
## [203] {neural,demonstr} => {network} 0.1372549 1.0000000
## [204] {network,demonstr} => {neural} 0.1372549 0.8750000
## [205] {set,larg} => {network} 0.1176471 1.0000000
## [206] {convolut,set} => {network} 0.1176471 1.0000000
## [207] {paper,set} => {network} 0.1078431 0.8461538
## [208] {train,set} => {network} 0.1078431 0.8461538
## [209] {model,set} => {network} 0.1078431 0.9166667
## [210] {neural,set} => {network} 0.1176471 1.0000000
## [211] {perform,effici} => {network} 0.1078431 0.7857143
## [212] {neural,effici} => {network} 0.1078431 1.0000000
## [213] {object,propos} => {network} 0.1078431 1.0000000
## [214] {object,train} => {network} 0.1078431 0.9166667
## [215] {neural,object} => {network} 0.1078431 0.9166667
## [216] {dataset,larg} => {network} 0.1176471 1.0000000
## [217] {approach,larg} => {network} 0.1078431 1.0000000
## [218] {propos,larg} => {network} 0.1078431 1.0000000
## [219] {convolut,larg} => {network} 0.1274510 1.0000000
## [220] {show,larg} => {neural} 0.1078431 0.7333333
## [221] {show,larg} => {network} 0.1470588 1.0000000
## [222] {train,larg} => {neural} 0.1078431 0.7857143
## [223] {train,larg} => {network} 0.1372549 1.0000000
## [224] {model,larg} => {network} 0.1274510 0.9285714
## [225] {neural,larg} => {network} 0.1568627 1.0000000
## [226] {network,larg} => {neural} 0.1568627 0.7272727
## [227] {show,evalu} => {network} 0.1078431 0.7333333
## [228] {network,evalu} => {show} 0.1078431 0.7333333
## [229] {neural,evalu} => {network} 0.1078431 0.9166667
## [230] {network,evalu} => {neural} 0.1078431 0.7333333
## [231] {neural,techniqu} => {network} 0.1372549 1.0000000
## [232] {network,techniqu} => {neural} 0.1372549 0.8235294
## [233] {compar,convolut} => {network} 0.1078431 0.9166667
## [234] {compar,result} => {perform} 0.1078431 0.7333333
## [235] {compar,train} => {perform} 0.1078431 0.7857143
## [236] {compar,perform} => {network} 0.1176471 0.7058824
## [237] {compar,network} => {perform} 0.1176471 0.7500000
## [238] {train,framework} => {perform} 0.1176471 0.8571429
## [239] {perform,framework} => {train} 0.1176471 0.7500000
## [240] {perform,framework} => {network} 0.1176471 0.7500000
## [241] {model,framework} => {neural} 0.1078431 0.7857143
## [242] {model,framework} => {network} 0.1176471 0.8571429
## [243] {neural,framework} => {network} 0.1666667 1.0000000
## [244] {network,framework} => {neural} 0.1666667 0.8500000
## [245] {applic,train} => {network} 0.1078431 0.7333333
## [246] {applic,neural} => {network} 0.1470588 1.0000000
## [247] {applic,network} => {neural} 0.1470588 0.9375000
## [248] {process,task} => {neural} 0.1078431 0.9166667
## [249] {process,task} => {network} 0.1078431 0.9166667
## [250] {perform,process} => {neural} 0.1176471 0.8571429
## [251] {neural,process} => {perform} 0.1176471 0.7058824
## [252] {perform,process} => {network} 0.1176471 0.8571429
## [253] {network,process} => {perform} 0.1176471 0.7058824
## [254] {neural,process} => {network} 0.1568627 0.9411765
## [255] {network,process} => {neural} 0.1568627 0.9411765
## [256] {imag,classif} => {network} 0.1078431 0.8461538
## [257] {convolut,classif} => {network} 0.1176471 0.9230769
## [258] {train,classif} => {network} 0.1274510 0.8666667
## [259] {model,classif} => {perform} 0.1078431 0.7333333
## [260] {perform,classif} => {network} 0.1176471 0.7500000
## [261] {model,classif} => {network} 0.1078431 0.7333333
## [262] {neural,classif} => {network} 0.1666667 0.9444444
## [263] {network,classif} => {neural} 0.1666667 0.8095238
## [264] {inform,neural} => {network} 0.1568627 1.0000000
## [265] {inform,network} => {neural} 0.1568627 0.8421053
## [266] {task,machin} => {neural} 0.1078431 0.8461538
## [267] {train,machin} => {perform} 0.1176471 0.8000000
## [268] {perform,machin} => {train} 0.1176471 0.7500000
## [269] {train,machin} => {network} 0.1176471 0.8000000
## [270] {perform,machin} => {network} 0.1274510 0.8125000
## [271] {model,machin} => {neural} 0.1078431 0.7857143
## [272] {model,machin} => {network} 0.1176471 0.8571429
## [273] {neural,machin} => {network} 0.1666667 0.9444444
## [274] {network,machin} => {neural} 0.1666667 0.8095238
## [275] {work,achiev} => {network} 0.1078431 1.0000000
## [276] {base,work} => {network} 0.1274510 0.9285714
## [277] {neural,work} => {network} 0.1470588 1.0000000
## [278] {network,work} => {neural} 0.1470588 0.7500000
## [279] {imag,present} => {neural} 0.1078431 0.9166667
## [280] {imag,present} => {network} 0.1176471 1.0000000
## [281] {present,achiev} => {network} 0.1176471 0.8571429
## [282] {present,data} => {network} 0.1078431 0.8461538
## [283] {convolut,present} => {neural} 0.1078431 0.8461538
## [284] {convolut,present} => {network} 0.1274510 1.0000000
## [285] {present,result} => {network} 0.1078431 0.7333333
## [286] {present,train} => {neural} 0.1470588 0.8333333
## [287] {neural,present} => {train} 0.1470588 0.7142857
## [288] {present,train} => {network} 0.1568627 0.8888889
## [289] {perform,present} => {network} 0.1176471 0.8000000
## [290] {model,present} => {neural} 0.1176471 0.8000000
## [291] {model,present} => {network} 0.1176471 0.8000000
## [292] {neural,present} => {network} 0.2058824 1.0000000
## [293] {network,present} => {neural} 0.2058824 0.8750000
## [294] {accuraci,improv} => {show} 0.1078431 0.8461538
## [295] {show,accuraci} => {improv} 0.1078431 0.7333333
## [296] {accuraci,achiev} => {neural} 0.1078431 0.8461538
## [297] {accuraci,achiev} => {network} 0.1274510 1.0000000
## [298] {accuraci,data} => {neural} 0.1176471 0.8571429
## [299] {accuraci,data} => {network} 0.1274510 0.9285714
## [300] {convolut,accuraci} => {network} 0.1176471 1.0000000
## [301] {accuraci,result} => {neural} 0.1176471 0.7058824
## [302] {accuraci,result} => {network} 0.1176471 0.7058824
## [303] {show,accuraci} => {network} 0.1176471 0.8000000
## [304] {model,accuraci} => {train} 0.1176471 0.8000000
## [305] {train,accuraci} => {neural} 0.1372549 0.7368421
## [306] {neural,accuraci} => {train} 0.1372549 0.7000000
## [307] {train,accuraci} => {network} 0.1470588 0.7894737
## [308] {perform,accuraci} => {network} 0.1176471 0.8571429
## [309] {model,accuraci} => {network} 0.1176471 0.8000000
## [310] {neural,accuraci} => {network} 0.1960784 1.0000000
## [311] {network,accuraci} => {neural} 0.1960784 0.8695652
## [312] {dataset,improv} => {show} 0.1078431 0.7857143
## [313] {dataset,improv} => {train} 0.1078431 0.7857143
## [314] {dataset,improv} => {network} 0.1078431 0.7857143
## [315] {imag,dataset} => {train} 0.1274510 0.8666667
## [316] {imag,dataset} => {network} 0.1274510 0.8666667
## [317] {dataset,propos} => {show} 0.1078431 0.7333333
## [318] {dataset,propos} => {train} 0.1078431 0.7333333
## [319] {convolut,dataset} => {network} 0.1372549 0.9333333
## [320] {dataset,result} => {train} 0.1078431 0.7333333
## [321] {show,dataset} => {train} 0.1568627 0.8421053
## [322] {show,dataset} => {network} 0.1470588 0.7894737
## [323] {perform,dataset} => {train} 0.1470588 0.8333333
## [324] {model,dataset} => {train} 0.1274510 0.7222222
## [325] {neural,dataset} => {train} 0.1372549 0.7777778
## [326] {train,dataset} => {network} 0.1764706 0.7826087
## [327] {network,dataset} => {train} 0.1764706 0.7826087
## [328] {model,dataset} => {network} 0.1274510 0.7222222
## [329] {neural,dataset} => {network} 0.1764706 1.0000000
## [330] {network,dataset} => {neural} 0.1764706 0.7826087
## [331] {improv,architectur} => {network} 0.1176471 0.8571429
## [332] {comput,architectur} => {network} 0.1078431 0.8461538
## [333] {convolut,architectur} => {network} 0.1274510 0.9285714
## [334] {architectur,result} => {perform} 0.1274510 0.7647059
## [335] {architectur,result} => {network} 0.1176471 0.7058824
## [336] {architectur,paper} => {network} 0.1078431 0.7857143
## [337] {show,architectur} => {neural} 0.1078431 0.8461538
## [338] {show,architectur} => {network} 0.1078431 0.8461538
## [339] {train,architectur} => {perform} 0.1274510 0.7647059
## [340] {train,architectur} => {neural} 0.1372549 0.8235294
## [341] {neural,architectur} => {train} 0.1372549 0.7000000
## [342] {train,architectur} => {network} 0.1666667 1.0000000
## [343] {perform,architectur} => {network} 0.1470588 0.7500000
## [344] {model,architectur} => {neural} 0.1176471 0.8571429
## [345] {model,architectur} => {network} 0.1078431 0.7857143
## [346] {neural,architectur} => {network} 0.1862745 0.9500000
## [347] {network,architectur} => {neural} 0.1862745 0.7600000
## [348] {comput,improv} => {network} 0.1078431 0.8461538
## [349] {convolut,improv} => {network} 0.1176471 1.0000000
## [350] {improv,result} => {perform} 0.1078431 0.7857143
## [351] {show,improv} => {neural} 0.1274510 0.7222222
## [352] {show,improv} => {network} 0.1372549 0.7777778
## [353] {train,improv} => {perform} 0.1470588 0.7500000
## [354] {perform,improv} => {train} 0.1470588 0.7894737
## [355] {train,improv} => {network} 0.1568627 0.8000000
## [356] {neural,improv} => {network} 0.1764706 0.9000000
## [357] {network,improv} => {neural} 0.1764706 0.7826087
## [358] {imag,task} => {network} 0.1078431 1.0000000
## [359] {base,imag} => {network} 0.1078431 0.9166667
## [360] {approach,imag} => {network} 0.1176471 1.0000000
## [361] {imag,comput} => {network} 0.1176471 0.8000000
## [362] {convolut,imag} => {network} 0.1862745 1.0000000
## [363] {imag,network} => {convolut} 0.1862745 0.7037037
## [364] {imag,method} => {network} 0.1078431 0.7857143
## [365] {imag,result} => {network} 0.1078431 0.7857143
## [366] {imag,paper} => {network} 0.1176471 0.8000000
## [367] {imag,show} => {network} 0.1176471 0.8571429
## [368] {imag,perform} => {train} 0.1176471 0.8000000
## [369] {imag,train} => {neural} 0.1470588 0.7142857
## [370] {imag,neural} => {train} 0.1470588 0.7500000
## [371] {imag,train} => {network} 0.1862745 0.9047619
## [372] {imag,network} => {train} 0.1862745 0.7037037
## [373] {imag,perform} => {network} 0.1372549 0.9333333
## [374] {imag,model} => {neural} 0.1176471 0.7500000
## [375] {imag,model} => {network} 0.1372549 0.8750000
## [376] {imag,neural} => {network} 0.1960784 1.0000000
## [377] {imag,network} => {neural} 0.1960784 0.7407407
## [378] {base,achiev} => {network} 0.1078431 0.9166667
## [379] {achiev,featur} => {network} 0.1274510 1.0000000
## [380] {achiev,propos} => {network} 0.1274510 0.9285714
## [381] {achiev,data} => {network} 0.1372549 0.8235294
## [382] {achiev,method} => {convolut} 0.1078431 0.7333333
## [383] {convolut,method} => {achiev} 0.1078431 0.7857143
## [384] {convolut,achiev} => {network} 0.1666667 1.0000000
## [385] {achiev,method} => {network} 0.1372549 0.9333333
## [386] {achiev,paper} => {network} 0.1372549 0.7368421
## [387] {train,achiev} => {neural} 0.1078431 0.7333333
## [388] {train,achiev} => {network} 0.1274510 0.8666667
## [389] {perform,achiev} => {network} 0.1470588 0.8333333
## [390] {model,achiev} => {network} 0.1274510 0.8125000
## [391] {neural,achiev} => {network} 0.1960784 0.9523810
## [392] {network,achiev} => {neural} 0.1960784 0.7142857
## [393] {base,task} => {neural} 0.1078431 0.8461538
## [394] {base,task} => {network} 0.1078431 0.8461538
## [395] {task,propos} => {network} 0.1078431 0.8461538
## [396] {convolut,task} => {network} 0.1078431 0.9166667
## [397] {task,paper} => {neural} 0.1274510 0.7647059
## [398] {task,paper} => {network} 0.1274510 0.7647059
## [399] {show,task} => {train} 0.1176471 0.7500000
## [400] {task,train} => {show} 0.1176471 0.7500000
## [401] {show,task} => {network} 0.1176471 0.7500000
## [402] {task,train} => {perform} 0.1176471 0.7500000
## [403] {task,train} => {network} 0.1274510 0.8125000
## [404] {model,task} => {neural} 0.1470588 0.7500000
## [405] {model,task} => {network} 0.1568627 0.8000000
## [406] {neural,task} => {network} 0.2058824 0.9130435
## [407] {network,task} => {neural} 0.2058824 0.8750000
## [408] {base,featur} => {network} 0.1372549 0.9333333
## [409] {approach,base} => {network} 0.1176471 1.0000000
## [410] {base,convolut} => {network} 0.1372549 1.0000000
## [411] {base,train} => {network} 0.1372549 0.7777778
## [412] {base,perform} => {network} 0.1274510 1.0000000
## [413] {base,model} => {neural} 0.1274510 0.7222222
## [414] {base,model} => {network} 0.1568627 0.8888889
## [415] {base,neural} => {network} 0.1862745 0.9500000
## [416] {approach,featur} => {network} 0.1372549 0.7777778
## [417] {propos,featur} => {model} 0.1078431 0.7333333
## [418] {propos,featur} => {neural} 0.1078431 0.7333333
## [419] {propos,featur} => {network} 0.1274510 0.8666667
## [420] {data,featur} => {network} 0.1078431 0.7857143
## [421] {convolut,featur} => {network} 0.1176471 1.0000000
## [422] {method,featur} => {paper} 0.1078431 0.7333333
## [423] {method,featur} => {model} 0.1176471 0.8000000
## [424] {method,featur} => {network} 0.1078431 0.7333333
## [425] {featur,result} => {model} 0.1078431 0.7333333
## [426] {featur,result} => {neural} 0.1078431 0.7333333
## [427] {featur,result} => {network} 0.1274510 0.8666667
## [428] {paper,featur} => {model} 0.1274510 0.7222222
## [429] {paper,featur} => {network} 0.1274510 0.7222222
## [430] {show,featur} => {neural} 0.1176471 0.7058824
## [431] {show,featur} => {network} 0.1470588 0.8823529
## [432] {train,featur} => {model} 0.1078431 0.7857143
## [433] {perform,featur} => {network} 0.1274510 0.7647059
## [434] {model,featur} => {neural} 0.1568627 0.7272727
## [435] {neural,featur} => {model} 0.1568627 0.7272727
## [436] {model,featur} => {network} 0.1764706 0.8181818
## [437] {neural,featur} => {network} 0.2156863 1.0000000
## [438] {network,featur} => {neural} 0.2156863 0.7857143
## [439] {approach,propos} => {network} 0.1372549 0.7777778
## [440] {approach,convolut} => {network} 0.1274510 0.9285714
## [441] {approach,result} => {network} 0.1176471 0.7058824
## [442] {approach,paper} => {show} 0.1176471 0.7058824
## [443] {approach,paper} => {network} 0.1274510 0.7647059
## [444] {approach,show} => {network} 0.1372549 0.7777778
## [445] {approach,train} => {network} 0.1372549 0.7368421
## [446] {approach,model} => {neural} 0.1176471 0.7500000
## [447] {approach,model} => {network} 0.1274510 0.8125000
## [448] {approach,neural} => {network} 0.2058824 1.0000000
## [449] {approach,network} => {neural} 0.2058824 0.7777778
## [450] {algorithm,comput} => {network} 0.1372549 0.8750000
## [451] {algorithm,data} => {perform} 0.1078431 0.8461538
## [452] {convolut,algorithm} => {network} 0.1078431 1.0000000
## [453] {train,algorithm} => {network} 0.1666667 0.8095238
## [454] {perform,algorithm} => {network} 0.1470588 0.7142857
## [455] {model,algorithm} => {network} 0.1274510 0.7647059
## [456] {neural,algorithm} => {network} 0.1960784 0.9523810
## [457] {network,algorithm} => {neural} 0.1960784 0.7692308
## [458] {convolut,comput} => {network} 0.1862745 0.9500000
## [459] {comput,result} => {network} 0.1176471 0.7500000
## [460] {comput,paper} => {network} 0.1470588 0.7500000
## [461] {show,comput} => {network} 0.1568627 0.8421053
## [462] {train,comput} => {neural} 0.1274510 0.7222222
## [463] {train,comput} => {network} 0.1470588 0.8333333
## [464] {perform,comput} => {network} 0.1372549 0.7777778
## [465] {model,comput} => {neural} 0.1176471 0.7058824
## [466] {model,comput} => {network} 0.1372549 0.8235294
## [467] {neural,comput} => {network} 0.2352941 1.0000000
## [468] {network,comput} => {neural} 0.2352941 0.8000000
## [469] {data,propos} => {network} 0.1274510 0.8125000
## [470] {convolut,propos} => {neural} 0.1176471 0.8000000
## [471] {convolut,propos} => {network} 0.1470588 1.0000000
## [472] {method,propos} => {network} 0.1372549 0.7000000
## [473] {propos,result} => {network} 0.1274510 0.7222222
## [474] {propos,paper} => {network} 0.1568627 0.7619048
## [475] {show,propos} => {network} 0.1568627 0.7619048
## [476] {perform,propos} => {train} 0.1372549 0.7000000
## [477] {train,propos} => {network} 0.1568627 0.7272727
## [478] {perform,propos} => {network} 0.1372549 0.7000000
## [479] {model,propos} => {network} 0.1568627 0.7619048
## [480] {neural,propos} => {network} 0.2352941 1.0000000
## [481] {network,propos} => {neural} 0.2352941 0.8275862
## [482] {convolut,data} => {network} 0.1274510 1.0000000
## [483] {data,method} => {perform} 0.1176471 0.7500000
## [484] {data,method} => {model} 0.1176471 0.7500000
## [485] {data,result} => {network} 0.1862745 0.7916667
## [486] {data,paper} => {perform} 0.1470588 0.7142857
## [487] {show,data} => {perform} 0.1372549 0.7000000
## [488] {show,data} => {network} 0.1568627 0.8000000
## [489] {train,data} => {neural} 0.1372549 0.7368421
## [490] {train,data} => {network} 0.1470588 0.7894737
## [491] {neural,data} => {network} 0.2254902 1.0000000
## [492] {network,data} => {neural} 0.2254902 0.7931034
## [493] {convolut,method} => {network} 0.1372549 1.0000000
## [494] {convolut,result} => {neural} 0.1372549 0.7777778
## [495] {convolut,result} => {network} 0.1666667 0.9444444
## [496] {convolut,paper} => {network} 0.2156863 1.0000000
## [497] {convolut,show} => {neural} 0.1372549 0.7000000
## [498] {convolut,show} => {network} 0.1960784 1.0000000
## [499] {convolut,train} => {neural} 0.1274510 0.7222222
## [500] {convolut,train} => {network} 0.1764706 1.0000000
## [501] {convolut,perform} => {network} 0.1862745 0.9500000
## [502] {convolut,model} => {neural} 0.1372549 0.7777778
## [503] {convolut,model} => {network} 0.1764706 1.0000000
## [504] {convolut,neural} => {network} 0.2647059 1.0000000
## [505] {convolut,network} => {neural} 0.2647059 0.7105263
## [506] {show,method} => {model} 0.1470588 0.7142857
## [507] {train,method} => {network} 0.1470588 0.7142857
## [508] {neural,method} => {network} 0.2058824 0.9545455
## [509] {network,method} => {neural} 0.2058824 0.7777778
## [510] {show,result} => {paper} 0.1764706 0.7500000
## [511] {paper,result} => {network} 0.1960784 0.7142857
## [512] {show,result} => {network} 0.1666667 0.7083333
## [513] {neural,result} => {network} 0.2745098 0.9655172
## [514] {network,result} => {neural} 0.2745098 0.8484848
## [515] {train,paper} => {network} 0.1666667 0.7391304
## [516] {neural,paper} => {network} 0.2549020 0.9285714
## [517] {network,paper} => {neural} 0.2549020 0.7647059
## [518] {show,train} => {network} 0.1862745 0.7600000
## [519] {perform,show} => {network} 0.1862745 0.7307692
## [520] {model,show} => {network} 0.2058824 0.7777778
## [521] {neural,show} => {network} 0.2941176 0.9677419
## [522] {network,show} => {neural} 0.2941176 0.7894737
## [523] {perform,train} => {network} 0.2450980 0.8064516
## [524] {model,train} => {network} 0.2156863 0.7586207
## [525] {neural,train} => {network} 0.3235294 1.0000000
## [526] {network,train} => {neural} 0.3235294 0.8250000
## [527] {neural,perform} => {network} 0.2549020 0.9629630
## [528] {network,perform} => {neural} 0.2549020 0.7027027
## [529] {model,neural} => {network} 0.3431373 0.9722222
## [530] {model,network} => {neural} 0.3431373 0.8750000
## [531] {cnn,convolut,neural} => {network} 0.1078431 1.0000000
## [532] {cnn,convolut,network} => {neural} 0.1078431 1.0000000
## [533] {cnn,network,neural} => {convolut} 0.1078431 0.9166667
## [534] {neural,extract,featur} => {network} 0.1078431 1.0000000
## [535] {network,extract,featur} => {neural} 0.1078431 0.8461538
## [536] {network,neural,extract} => {featur} 0.1078431 0.7857143
## [537] {model,neural,visual} => {network} 0.1176471 1.0000000
## [538] {model,network,visual} => {neural} 0.1176471 0.8571429
## [539] {network,neural,visual} => {model} 0.1176471 0.9230769
## [540] {neural,classif,classifi} => {network} 0.1078431 1.0000000
## [541] {network,classif,classifi} => {neural} 0.1078431 0.8461538
## [542] {neural,featur,classifi} => {network} 0.1078431 1.0000000
## [543] {network,featur,classifi} => {neural} 0.1078431 0.9166667
## [544] {approach,neural,classifi} => {network} 0.1078431 1.0000000
## [545] {approach,network,classifi} => {neural} 0.1078431 0.8461538
## [546] {neural,paper,problem} => {network} 0.1176471 1.0000000
## [547] {network,paper,problem} => {neural} 0.1176471 0.8571429
## [548] {network,neural,problem} => {paper} 0.1176471 0.8000000
## [549] {neural,show,larg} => {network} 0.1078431 1.0000000
## [550] {network,show,larg} => {neural} 0.1078431 0.7333333
## [551] {neural,train,larg} => {network} 0.1078431 1.0000000
## [552] {network,train,larg} => {neural} 0.1078431 0.7857143
## [553] {model,neural,framework} => {network} 0.1078431 1.0000000
## [554] {model,network,framework} => {neural} 0.1078431 0.9166667
## [555] {neural,perform,process} => {network} 0.1078431 0.9166667
## [556] {network,perform,process} => {neural} 0.1078431 0.9166667
## [557] {inform,model,neural} => {network} 0.1078431 1.0000000
## [558] {inform,model,network} => {neural} 0.1078431 1.0000000
## [559] {model,neural,machin} => {network} 0.1078431 1.0000000
## [560] {model,network,machin} => {neural} 0.1078431 0.9166667
## [561] {imag,neural,present} => {network} 0.1078431 1.0000000
## [562] {imag,network,present} => {neural} 0.1078431 0.9166667
## [563] {convolut,neural,present} => {network} 0.1078431 1.0000000
## [564] {convolut,network,present} => {neural} 0.1078431 0.8461538
## [565] {neural,present,train} => {network} 0.1470588 1.0000000
## [566] {network,present,train} => {neural} 0.1470588 0.9375000
## [567] {network,neural,present} => {train} 0.1470588 0.7142857
## [568] {model,neural,present} => {network} 0.1176471 1.0000000
## [569] {model,network,present} => {neural} 0.1176471 1.0000000
## [570] {neural,accuraci,achiev} => {network} 0.1078431 1.0000000
## [571] {network,accuraci,achiev} => {neural} 0.1078431 0.8461538
## [572] {neural,accuraci,data} => {network} 0.1176471 1.0000000
## [573] {network,accuraci,data} => {neural} 0.1176471 0.9230769
## [574] {neural,accuraci,result} => {network} 0.1176471 1.0000000
## [575] {network,accuraci,result} => {neural} 0.1176471 1.0000000
## [576] {neural,train,accuraci} => {network} 0.1372549 1.0000000
## [577] {network,train,accuraci} => {neural} 0.1372549 0.9333333
## [578] {network,neural,accuraci} => {train} 0.1372549 0.7000000
## [579] {imag,train,dataset} => {network} 0.1078431 0.8461538
## [580] {imag,network,dataset} => {train} 0.1078431 0.8461538
## [581] {perform,show,dataset} => {train} 0.1078431 1.0000000
## [582] {perform,train,dataset} => {show} 0.1078431 0.7333333
## [583] {show,train,dataset} => {network} 0.1176471 0.7500000
## [584] {network,show,dataset} => {train} 0.1176471 0.8000000
## [585] {neural,show,dataset} => {network} 0.1176471 1.0000000
## [586] {network,show,dataset} => {neural} 0.1176471 0.8000000
## [587] {perform,train,dataset} => {network} 0.1176471 0.8000000
## [588] {network,perform,dataset} => {train} 0.1176471 1.0000000
## [589] {neural,train,dataset} => {network} 0.1372549 1.0000000
## [590] {network,train,dataset} => {neural} 0.1372549 0.7777778
## [591] {network,neural,dataset} => {train} 0.1372549 0.7777778
## [592] {perform,train,architectur} => {network} 0.1274510 1.0000000
## [593] {network,train,architectur} => {perform} 0.1274510 0.7647059
## [594] {network,perform,architectur} => {train} 0.1274510 0.8666667
## [595] {neural,train,architectur} => {network} 0.1372549 1.0000000
## [596] {network,train,architectur} => {neural} 0.1372549 0.8235294
## [597] {network,neural,architectur} => {train} 0.1372549 0.7368421
## [598] {neural,perform,architectur} => {network} 0.1078431 0.9166667
## [599] {network,perform,architectur} => {neural} 0.1078431 0.7333333
## [600] {model,neural,architectur} => {network} 0.1078431 0.9166667
## [601] {model,network,architectur} => {neural} 0.1078431 1.0000000
## [602] {neural,show,improv} => {network} 0.1176471 0.9230769
## [603] {network,show,improv} => {neural} 0.1176471 0.8571429
## [604] {perform,train,improv} => {network} 0.1176471 0.8000000
## [605] {network,train,improv} => {perform} 0.1176471 0.7500000
## [606] {network,perform,improv} => {train} 0.1176471 0.9230769
## [607] {neural,train,improv} => {network} 0.1274510 1.0000000
## [608] {network,train,improv} => {neural} 0.1274510 0.8125000
## [609] {network,neural,improv} => {train} 0.1274510 0.7222222
## [610] {convolut,imag,train} => {network} 0.1176471 1.0000000
## [611] {convolut,imag,neural} => {network} 0.1274510 1.0000000
## [612] {imag,perform,train} => {network} 0.1078431 0.9166667
## [613] {imag,network,perform} => {train} 0.1078431 0.7857143
## [614] {imag,neural,train} => {network} 0.1470588 1.0000000
## [615] {imag,network,train} => {neural} 0.1470588 0.7894737
## [616] {imag,network,neural} => {train} 0.1470588 0.7500000
## [617] {imag,model,neural} => {network} 0.1176471 1.0000000
## [618] {imag,model,network} => {neural} 0.1176471 0.8571429
## [619] {convolut,achiev,method} => {network} 0.1078431 1.0000000
## [620] {network,achiev,method} => {convolut} 0.1078431 0.7857143
## [621] {convolut,network,method} => {achiev} 0.1078431 0.7857143
## [622] {convolut,achiev,paper} => {network} 0.1078431 1.0000000
## [623] {network,achiev,paper} => {convolut} 0.1078431 0.7857143
## [624] {convolut,neural,achiev} => {network} 0.1078431 1.0000000
## [625] {neural,achiev,paper} => {network} 0.1078431 0.9166667
## [626] {network,achiev,paper} => {neural} 0.1078431 0.7857143
## [627] {neural,train,achiev} => {network} 0.1078431 1.0000000
## [628] {network,train,achiev} => {neural} 0.1078431 0.8461538
## [629] {neural,task,paper} => {network} 0.1078431 0.8461538
## [630] {network,task,paper} => {neural} 0.1078431 0.8461538
## [631] {neural,task,train} => {network} 0.1078431 1.0000000
## [632] {network,task,train} => {neural} 0.1078431 0.8461538
## [633] {model,neural,task} => {network} 0.1372549 0.9333333
## [634] {model,network,task} => {neural} 0.1372549 0.8750000
## [635] {base,model,neural} => {network} 0.1274510 1.0000000
## [636] {base,model,network} => {neural} 0.1274510 0.8125000
## [637] {approach,neural,featur} => {network} 0.1078431 1.0000000
## [638] {approach,network,featur} => {neural} 0.1078431 0.7857143
## [639] {neural,propos,featur} => {network} 0.1078431 1.0000000
## [640] {network,propos,featur} => {neural} 0.1078431 0.8461538
## [641] {neural,featur,result} => {network} 0.1078431 1.0000000
## [642] {network,featur,result} => {neural} 0.1078431 0.8461538
## [643] {neural,show,featur} => {network} 0.1176471 1.0000000
## [644] {network,show,featur} => {neural} 0.1176471 0.8000000
## [645] {model,neural,featur} => {network} 0.1568627 1.0000000
## [646] {model,network,featur} => {neural} 0.1568627 0.8888889
## [647] {network,neural,featur} => {model} 0.1568627 0.7272727
## [648] {approach,model,neural} => {network} 0.1176471 1.0000000
## [649] {approach,model,network} => {neural} 0.1176471 0.9230769
## [650] {neural,algorithm,comput} => {network} 0.1078431 1.0000000
## [651] {network,algorithm,comput} => {neural} 0.1078431 0.7857143
## [652] {perform,train,algorithm} => {network} 0.1078431 0.7857143
## [653] {network,perform,algorithm} => {train} 0.1078431 0.7333333
## [654] {neural,train,algorithm} => {network} 0.1274510 1.0000000
## [655] {network,train,algorithm} => {neural} 0.1274510 0.7647059
## [656] {convolut,comput,paper} => {network} 0.1078431 1.0000000
## [657] {network,comput,paper} => {convolut} 0.1078431 0.7333333
## [658] {convolut,show,comput} => {network} 0.1176471 1.0000000
## [659] {network,show,comput} => {convolut} 0.1176471 0.7500000
## [660] {convolut,neural,comput} => {network} 0.1274510 1.0000000
## [661] {neural,comput,paper} => {network} 0.1078431 1.0000000
## [662] {network,comput,paper} => {neural} 0.1078431 0.7333333
## [663] {neural,show,comput} => {network} 0.1176471 1.0000000
## [664] {network,show,comput} => {neural} 0.1176471 0.7500000
## [665] {neural,train,comput} => {network} 0.1274510 1.0000000
## [666] {network,train,comput} => {neural} 0.1274510 0.8666667
## [667] {model,neural,comput} => {network} 0.1176471 1.0000000
## [668] {model,network,comput} => {neural} 0.1176471 0.8571429
## [669] {neural,data,propos} => {network} 0.1078431 1.0000000
## [670] {network,data,propos} => {neural} 0.1078431 0.8461538
## [671] {convolut,neural,propos} => {network} 0.1176471 1.0000000
## [672] {convolut,network,propos} => {neural} 0.1176471 0.8000000
## [673] {neural,method,propos} => {network} 0.1176471 1.0000000
## [674] {network,method,propos} => {neural} 0.1176471 0.8571429
## [675] {neural,propos,result} => {network} 0.1176471 1.0000000
## [676] {network,propos,result} => {neural} 0.1176471 0.9230769
## [677] {neural,propos,paper} => {network} 0.1274510 1.0000000
## [678] {network,propos,paper} => {neural} 0.1274510 0.8125000
## [679] {neural,show,propos} => {network} 0.1176471 1.0000000
## [680] {network,show,propos} => {neural} 0.1176471 0.7500000
## [681] {neural,train,propos} => {network} 0.1274510 1.0000000
## [682] {network,train,propos} => {neural} 0.1274510 0.8125000
## [683] {model,neural,propos} => {network} 0.1372549 1.0000000
## [684] {model,network,propos} => {neural} 0.1372549 0.8750000
## [685] {neural,data,result} => {network} 0.1568627 1.0000000
## [686] {network,data,result} => {neural} 0.1568627 0.8421053
## [687] {neural,show,data} => {network} 0.1078431 1.0000000
## [688] {neural,train,data} => {network} 0.1372549 1.0000000
## [689] {network,train,data} => {neural} 0.1372549 0.9333333
## [690] {neural,perform,data} => {network} 0.1176471 1.0000000
## [691] {network,perform,data} => {neural} 0.1176471 0.8000000
## [692] {model,neural,data} => {network} 0.1078431 1.0000000
## [693] {model,network,data} => {neural} 0.1078431 0.7857143
## [694] {convolut,paper,result} => {network} 0.1176471 1.0000000
## [695] {convolut,network,result} => {paper} 0.1176471 0.7058824
## [696] {convolut,neural,result} => {network} 0.1372549 1.0000000
## [697] {convolut,network,result} => {neural} 0.1372549 0.8235294
## [698] {convolut,show,paper} => {network} 0.1176471 1.0000000
## [699] {convolut,perform,paper} => {network} 0.1078431 1.0000000
## [700] {convolut,neural,paper} => {network} 0.1470588 1.0000000
## [701] {convolut,perform,show} => {network} 0.1176471 1.0000000
## [702] {convolut,model,show} => {network} 0.1078431 1.0000000
## [703] {convolut,neural,show} => {network} 0.1372549 1.0000000
## [704] {convolut,network,show} => {neural} 0.1372549 0.7000000
## [705] {convolut,neural,train} => {network} 0.1274510 1.0000000
## [706] {convolut,network,train} => {neural} 0.1274510 0.7222222
## [707] {convolut,model,neural} => {network} 0.1372549 1.0000000
## [708] {convolut,model,network} => {neural} 0.1372549 0.7777778
## [709] {neural,method,result} => {network} 0.1078431 0.9166667
## [710] {network,method,result} => {neural} 0.1078431 0.8461538
## [711] {perform,method,paper} => {model} 0.1078431 0.7333333
## [712] {model,perform,method} => {paper} 0.1078431 0.7333333
## [713] {neural,method,paper} => {network} 0.1176471 0.9230769
## [714] {network,method,paper} => {neural} 0.1176471 0.7500000
## [715] {perform,show,method} => {model} 0.1078431 0.7857143
## [716] {model,show,method} => {perform} 0.1078431 0.7333333
## [717] {model,perform,method} => {show} 0.1078431 0.7333333
## [718] {neural,train,method} => {network} 0.1274510 1.0000000
## [719] {network,train,method} => {neural} 0.1274510 0.8666667
## [720] {model,neural,method} => {network} 0.1274510 0.9285714
## [721] {model,network,method} => {neural} 0.1274510 0.8125000
## [722] {perform,paper,result} => {show} 0.1078431 0.7857143
## [723] {perform,show,result} => {paper} 0.1078431 0.7333333
## [724] {neural,show,result} => {paper} 0.1176471 0.7500000
## [725] {neural,show,paper} => {result} 0.1176471 0.8571429
## [726] {show,paper,result} => {network} 0.1274510 0.7222222
## [727] {network,show,result} => {paper} 0.1274510 0.7647059
## [728] {network,show,paper} => {result} 0.1274510 0.7222222
## [729] {neural,paper,result} => {network} 0.1666667 0.9444444
## [730] {network,paper,result} => {neural} 0.1666667 0.8500000
## [731] {perform,show,result} => {model} 0.1078431 0.7333333
## [732] {model,show,result} => {perform} 0.1078431 0.8461538
## [733] {model,perform,result} => {show} 0.1078431 0.7333333
## [734] {neural,show,result} => {network} 0.1470588 0.9375000
## [735] {network,show,result} => {neural} 0.1470588 0.8823529
## [736] {neural,train,result} => {network} 0.1372549 1.0000000
## [737] {network,train,result} => {neural} 0.1372549 0.8235294
## [738] {neural,perform,result} => {network} 0.1274510 0.9285714
## [739] {network,perform,result} => {neural} 0.1274510 0.7647059
## [740] {model,neural,result} => {network} 0.1372549 0.9333333
## [741] {model,network,result} => {neural} 0.1372549 0.9333333
## [742] {model,show,paper} => {perform} 0.1078431 0.7857143
## [743] {perform,show,paper} => {network} 0.1176471 0.7058824
## [744] {neural,show,paper} => {network} 0.1274510 0.9285714
## [745] {network,show,paper} => {neural} 0.1274510 0.7222222
## [746] {perform,train,paper} => {network} 0.1078431 0.7857143
## [747] {neural,train,paper} => {network} 0.1274510 1.0000000
## [748] {network,train,paper} => {neural} 0.1274510 0.7647059
## [749] {neural,perform,paper} => {network} 0.1078431 0.9166667
## [750] {model,neural,paper} => {network} 0.1568627 0.9411765
## [751] {model,network,paper} => {neural} 0.1568627 0.8888889
## [752] {perform,show,train} => {model} 0.1176471 0.7500000
## [753] {model,show,train} => {perform} 0.1176471 0.8000000
## [754] {perform,show,train} => {network} 0.1176471 0.7500000
## [755] {model,show,train} => {network} 0.1078431 0.7333333
## [756] {neural,show,train} => {network} 0.1568627 1.0000000
## [757] {network,show,train} => {neural} 0.1568627 0.8421053
## [758] {neural,perform,show} => {model} 0.1078431 0.7857143
## [759] {model,perform,show} => {network} 0.1274510 0.7222222
## [760] {neural,perform,show} => {network} 0.1274510 0.9285714
## [761] {model,neural,show} => {network} 0.1666667 0.9444444
## [762] {model,network,show} => {neural} 0.1666667 0.8095238
## [763] {neural,perform,train} => {model} 0.1274510 0.7222222
## [764] {model,neural,perform} => {train} 0.1274510 0.7647059
## [765] {model,perform,train} => {network} 0.1568627 0.8000000
## [766] {model,network,train} => {perform} 0.1568627 0.7272727
## [767] {model,network,perform} => {train} 0.1568627 0.8000000
## [768] {neural,perform,train} => {network} 0.1764706 1.0000000
## [769] {network,perform,train} => {neural} 0.1764706 0.7200000
## [770] {model,neural,train} => {network} 0.1862745 1.0000000
## [771] {model,network,train} => {neural} 0.1862745 0.8636364
## [772] {model,neural,perform} => {network} 0.1568627 0.9411765
## [773] {model,network,perform} => {neural} 0.1568627 0.8000000
## [774] {neural,show,paper,result} => {network} 0.1078431 0.9166667
## [775] {network,show,paper,result} => {neural} 0.1078431 0.8461538
## [776] {network,neural,show,result} => {paper} 0.1078431 0.7333333
## [777] {network,neural,show,paper} => {result} 0.1078431 0.8461538
## [778] {model,neural,perform,train} => {network} 0.1274510 1.0000000
## [779] {model,network,perform,train} => {neural} 0.1274510 0.8125000
## [780] {network,neural,perform,train} => {model} 0.1274510 0.7222222
## [781] {model,network,neural,perform} => {train} 0.1274510 0.8125000
## lift count
## [1] 1.7980769 11
## [2] 1.2466667 11
## [3] 1.3600000 12
## [4] 1.2466667 11
## [5] 1.8106509 12
## [6] 1.2466667 11
## [7] 1.2553846 12
## [8] 2.2130178 11
## [9] 1.5186104 12
## [10] 1.2553846 12
## [11] 0.9973333 11
## [12] 1.1786667 13
## [13] 1.4960000 11
## [14] 0.9973333 11
## [15] 1.2926267 11
## [16] 1.1657143 12
## [17] 1.4384615 11
## [18] 0.9973333 11
## [19] 2.0035714 11
## [20] 1.7142857 12
## [21] 1.5412088 11
## [22] 1.3161290 12
## [23] 1.1786667 13
## [24] 1.5000000 12
## [25] 1.4117647 12
## [26] 1.2580645 13
## [27] 1.1200000 14
## [28] 1.5111111 12
## [29] 1.2064516 11
## [30] 1.4400000 12
## [31] 1.3846154 12
## [32] 1.1200000 14
## [33] 1.4444444 13
## [34] 1.1612903 12
## [35] 1.2338710 12
## [36] 1.2750000 15
## [37] 2.3333333 14
## [38] 1.3709677 15
## [39] 1.2088889 16
## [40] 1.4117647 12
## [41] 1.3846154 12
## [42] 2.1818182 12
## [43] 1.5555556 14
## [44] 1.2580645 13
## [45] 1.3600000 17
## [46] 1.1333333 15
## [47] 1.4444444 13
## [48] 0.9600000 12
## [49] 1.0736842 15
## [50] 1.5000000 12
## [51] 1.4516129 15
## [52] 1.1200000 14
## [53] 2.5500000 14
## [54] 1.3161290 16
## [55] 1.2920000 19
## [56] 1.0736842 15
## [57] 1.0361905 16
## [58] 1.0736842 15
## [59] 1.3317972 17
## [60] 1.2304762 19
## [61] 1.7485714 18
## [62] 1.1751152 15
## [63] 1.1009524 17
## [64] 1.3918129 14
## [65] 1.1452632 16
## [66] 1.4571429 15
## [67] 1.3222222 14
## [68] 1.0880000 16
## [69] 1.9297297 14
## [70] 1.0880000 16
## [71] 1.0509091 17
## [72] 1.1234783 19
## [73] 1.0052174 17
## [74] 1.1009524 17
## [75] 1.3008696 22
## [76] 1.3894231 17
## [77] 1.0461538 20
## [78] 1.0200000 21
## [79] 0.9938462 19
## [80] 1.0577778 21
## [81] 1.0074074 20
## [82] 1.1913237 21
## [83] 1.1255172 24
## [84] 1.0786207 23
## [85] 1.4838710 23
## [86] 1.0090323 23
## [87] 1.0967742 25
## [88] 0.9775000 23
## [89] 1.1845161 27
## [90] 1.1539394 28
## [91] 0.9890909 24
## [92] 1.1539394 28
## [93] 1.1200000 28
## [94] 1.0200000 27
## [95] 0.9556757 26
## [96] 1.1027027 30
## [97] 1.0112821 29
## [98] 1.0112821 29
## [99] 1.3251282 38
## [100] 1.0766667 38
## [101] 1.0666667 40
## [102] 0.9676923 37
## [103] 1.0074074 40
## [104] 1.3161290 60
## [105] 1.3161290 60
## [106] 1.6451613 11
## [107] 2.3974359 11
## [108] 1.3600000 11
## [109] 2.3974359 11
## [110] 1.3600000 12
## [111] 1.6451613 12
## [112] 1.3600000 11
## [113] 1.5080645 11
## [114] 1.3600000 12
## [115] 1.5186104 12
## [116] 1.2553846 12
## [117] 1.4101382 12
## [118] 1.3600000 11
## [119] 1.2926267 11
## [120] 1.3600000 12
## [121] 1.3161290 12
## [122] 1.2926267 11
## [123] 2.2000000 11
## [124] 1.2628571 13
## [125] 2.4375000 13
## [126] 1.5080645 11
## [127] 1.3851852 11
## [128] 1.2693333 14
## [129] 1.4395161 14
## [130] 1.3600000 12
## [131] 2.1818182 12
## [132] 1.4101382 12
## [133] 1.7435897 12
## [134] 1.3600000 14
## [135] 1.5555556 14
## [136] 1.3600000 13
## [137] 1.2580645 13
## [138] 1.3600000 11
## [139] 1.2064516 11
## [140] 1.3600000 13
## [141] 1.4258065 13
## [142] 1.2693333 14
## [143] 1.6451613 14
## [144] 1.2926267 11
## [145] 1.2628571 13
## [146] 1.3600000 11
## [147] 1.5080645 11
## [148] 1.3600000 12
## [149] 1.3920596 11
## [150] 1.3600000 13
## [151] 1.3600000 12
## [152] 1.3600000 16
## [153] 1.3853990 16
## [154] 1.3600000 11
## [155] 1.9179487 11
## [156] 1.3600000 11
## [157] 1.2064516 11
## [158] 1.3600000 12
## [159] 1.2338710 12
## [160] 1.2466667 11
## [161] 1.5583333 11
## [162] 1.1507692 11
## [163] 1.3851852 11
## [164] 1.3600000 12
## [165] 1.3161290 12
## [166] 1.3600000 13
## [167] 1.5080645 11
## [168] 1.2466667 11
## [169] 1.2800000 16
## [170] 1.3853990 16
## [171] 1.8700000 11
## [172] 1.8700000 11
## [173] 2.0400000 11
## [174] 1.6320000 12
## [175] 1.0577778 14
## [176] 1.6800000 14
## [177] 1.3600000 15
## [178] 1.4516129 15
## [179] 1.2466667 11
## [180] 1.0685714 11
## [181] 1.3600000 12
## [182] 1.2338710 12
## [183] 1.3600000 12
## [184] 1.4101382 12
## [185] 1.1507692 11
## [186] 1.1657143 12
## [187] 1.4166667 12
## [188] 1.3600000 13
## [189] 1.3366935 13
## [190] 1.1657143 12
## [191] 2.0675676 12
## [192] 1.2466667 11
## [193] 1.2466667 11
## [194] 1.6923077 11
## [195] 2.4129032 11
## [196] 1.4666667 11
## [197] 1.9179487 11
## [198] 1.3600000 11
## [199] 1.3600000 11
## [200] 1.3600000 11
## [201] 1.3600000 15
## [202] 1.4516129 15
## [203] 1.3600000 14
## [204] 1.4395161 14
## [205] 1.3600000 12
## [206] 1.3600000 12
## [207] 1.1507692 11
## [208] 1.1507692 11
## [209] 1.2466667 11
## [210] 1.3600000 12
## [211] 1.0685714 11
## [212] 1.3600000 11
## [213] 1.3600000 11
## [214] 1.2466667 11
## [215] 1.2466667 11
## [216] 1.3600000 12
## [217] 1.3600000 11
## [218] 1.3600000 11
## [219] 1.3600000 13
## [220] 1.2064516 11
## [221] 1.3600000 15
## [222] 1.2926267 11
## [223] 1.3600000 14
## [224] 1.2628571 13
## [225] 1.3600000 16
## [226] 1.1964809 16
## [227] 0.9973333 11
## [228] 1.5583333 11
## [229] 1.2466667 11
## [230] 1.2064516 11
## [231] 1.3600000 14
## [232] 1.3548387 14
## [233] 1.2466667 11
## [234] 1.4384615 11
## [235] 1.5412088 11
## [236] 0.9600000 12
## [237] 1.4711538 12
## [238] 1.6813187 12
## [239] 1.5000000 12
## [240] 1.0200000 12
## [241] 1.2926267 11
## [242] 1.1657143 12
## [243] 1.3600000 17
## [244] 1.3983871 17
## [245] 0.9973333 11
## [246] 1.3600000 15
## [247] 1.5423387 15
## [248] 1.5080645 11
## [249] 1.2466667 11
## [250] 1.4101382 12
## [251] 1.3846154 12
## [252] 1.1657143 12
## [253] 1.3846154 12
## [254] 1.2800000 16
## [255] 1.5483871 16
## [256] 1.1507692 11
## [257] 1.2553846 12
## [258] 1.1786667 13
## [259] 1.4384615 11
## [260] 1.0200000 12
## [261] 0.9973333 11
## [262] 1.2844444 17
## [263] 1.3317972 17
## [264] 1.3600000 16
## [265] 1.3853990 16
## [266] 1.3920596 11
## [267] 1.5692308 12
## [268] 1.5000000 12
## [269] 1.0880000 12
## [270] 1.1050000 13
## [271] 1.2926267 11
## [272] 1.1657143 12
## [273] 1.2844444 17
## [274] 1.3317972 17
## [275] 1.3600000 11
## [276] 1.2628571 13
## [277] 1.3600000 15
## [278] 1.2338710 15
## [279] 1.5080645 11
## [280] 1.3600000 12
## [281] 1.1657143 12
## [282] 1.1507692 11
## [283] 1.3920596 11
## [284] 1.3600000 13
## [285] 0.9973333 11
## [286] 1.3709677 15
## [287] 1.4285714 15
## [288] 1.2088889 16
## [289] 1.0880000 12
## [290] 1.3161290 12
## [291] 1.0880000 12
## [292] 1.3600000 21
## [293] 1.4395161 21
## [294] 1.7980769 11
## [295] 2.3375000 11
## [296] 1.3920596 11
## [297] 1.3600000 13
## [298] 1.4101382 12
## [299] 1.2628571 13
## [300] 1.3600000 12
## [301] 1.1612903 12
## [302] 0.9600000 12
## [303] 1.0880000 12
## [304] 1.6000000 12
## [305] 1.2122241 14
## [306] 1.4000000 14
## [307] 1.0736842 15
## [308] 1.1657143 12
## [309] 1.0880000 12
## [310] 1.3600000 20
## [311] 1.4305750 20
## [312] 1.6696429 11
## [313] 1.5714286 11
## [314] 1.0685714 11
## [315] 1.7333333 13
## [316] 1.1786667 13
## [317] 1.5583333 11
## [318] 1.4666667 11
## [319] 1.2693333 14
## [320] 1.4666667 11
## [321] 1.6842105 16
## [322] 1.0736842 15
## [323] 1.6666667 15
## [324] 1.4444444 13
## [325] 1.5555556 14
## [326] 1.0643478 18
## [327] 1.5652174 18
## [328] 0.9822222 13
## [329] 1.3600000 18
## [330] 1.2875175 18
## [331] 1.1657143 12
## [332] 1.1507692 11
## [333] 1.2628571 13
## [334] 1.5000000 13
## [335] 0.9600000 12
## [336] 1.0685714 11
## [337] 1.3920596 11
## [338] 1.1507692 11
## [339] 1.5000000 13
## [340] 1.3548387 14
## [341] 1.4000000 14
## [342] 1.3600000 17
## [343] 1.0200000 15
## [344] 1.4101382 12
## [345] 1.0685714 11
## [346] 1.2920000 19
## [347] 1.2503226 19
## [348] 1.1507692 11
## [349] 1.3600000 12
## [350] 1.5412088 11
## [351] 1.1881720 13
## [352] 1.0577778 14
## [353] 1.4711538 15
## [354] 1.5789474 15
## [355] 1.0880000 16
## [356] 1.2240000 18
## [357] 1.2875175 18
## [358] 1.3600000 11
## [359] 1.2466667 11
## [360] 1.3600000 12
## [361] 1.0880000 12
## [362] 1.3600000 19
## [363] 1.8404558 19
## [364] 1.0685714 11
## [365] 1.0685714 11
## [366] 1.0880000 12
## [367] 1.1657143 12
## [368] 1.6000000 12
## [369] 1.1751152 15
## [370] 1.5000000 15
## [371] 1.2304762 19
## [372] 1.4074074 19
## [373] 1.2693333 14
## [374] 1.2338710 12
## [375] 1.1900000 14
## [376] 1.3600000 20
## [377] 1.2186380 20
## [378] 1.2466667 11
## [379] 1.3600000 13
## [380] 1.2628571 13
## [381] 1.1200000 14
## [382] 1.9179487 11
## [383] 2.4285714 11
## [384] 1.3600000 17
## [385] 1.2693333 14
## [386] 1.0021053 14
## [387] 1.2064516 11
## [388] 1.1786667 13
## [389] 1.1333333 15
## [390] 1.1050000 13
## [391] 1.2952381 20
## [392] 1.1751152 20
## [393] 1.3920596 11
## [394] 1.1507692 11
## [395] 1.1507692 11
## [396] 1.2466667 11
## [397] 1.2580645 13
## [398] 1.0400000 13
## [399] 1.5000000 12
## [400] 1.5937500 12
## [401] 1.0200000 12
## [402] 1.4711538 12
## [403] 1.1050000 13
## [404] 1.2338710 15
## [405] 1.0880000 16
## [406] 1.2417391 21
## [407] 1.4395161 21
## [408] 1.2693333 14
## [409] 1.3600000 12
## [410] 1.3600000 14
## [411] 1.0577778 14
## [412] 1.3600000 13
## [413] 1.1881720 13
## [414] 1.2088889 16
## [415] 1.2920000 19
## [416] 1.0577778 14
## [417] 1.3851852 11
## [418] 1.2064516 11
## [419] 1.1786667 13
## [420] 1.0685714 11
## [421] 1.3600000 12
## [422] 1.4960000 11
## [423] 1.5111111 12
## [424] 0.9973333 11
## [425] 1.3851852 11
## [426] 1.2064516 11
## [427] 1.1786667 13
## [428] 1.3641975 13
## [429] 0.9822222 13
## [430] 1.1612903 12
## [431] 1.2000000 15
## [432] 1.4841270 11
## [433] 1.0400000 13
## [434] 1.1964809 16
## [435] 1.3737374 16
## [436] 1.1127273 18
## [437] 1.3600000 22
## [438] 1.2926267 22
## [439] 1.0577778 14
## [440] 1.2628571 13
## [441] 0.9600000 12
## [442] 1.5000000 12
## [443] 1.0400000 13
## [444] 1.0577778 14
## [445] 1.0021053 14
## [446] 1.2338710 12
## [447] 1.1050000 13
## [448] 1.3600000 21
## [449] 1.2795699 21
## [450] 1.1900000 14
## [451] 1.6597633 11
## [452] 1.3600000 11
## [453] 1.1009524 17
## [454] 0.9714286 15
## [455] 1.0400000 13
## [456] 1.2952381 20
## [457] 1.2655087 20
## [458] 1.2920000 19
## [459] 1.0200000 12
## [460] 1.0200000 15
## [461] 1.1452632 16
## [462] 1.1881720 13
## [463] 1.1333333 15
## [464] 1.0577778 14
## [465] 1.1612903 12
## [466] 1.1200000 14
## [467] 1.3600000 24
## [468] 1.3161290 24
## [469] 1.1050000 13
## [470] 1.3161290 12
## [471] 1.3600000 15
## [472] 0.9520000 14
## [473] 0.9822222 13
## [474] 1.0361905 16
## [475] 1.0361905 16
## [476] 1.4000000 14
## [477] 0.9890909 16
## [478] 0.9520000 14
## [479] 1.0361905 16
## [480] 1.3600000 24
## [481] 1.3615128 24
## [482] 1.3600000 13
## [483] 1.4711538 12
## [484] 1.4166667 12
## [485] 1.0766667 19
## [486] 1.4010989 15
## [487] 1.3730769 14
## [488] 1.0880000 16
## [489] 1.2122241 14
## [490] 1.0736842 15
## [491] 1.3600000 23
## [492] 1.3047831 23
## [493] 1.3600000 14
## [494] 1.2795699 14
## [495] 1.2844444 17
## [496] 1.3600000 22
## [497] 1.1516129 14
## [498] 1.3600000 20
## [499] 1.1881720 13
## [500] 1.3600000 18
## [501] 1.2920000 19
## [502] 1.2795699 14
## [503] 1.3600000 18
## [504] 1.3600000 27
## [505] 1.1689304 27
## [506] 1.3492063 15
## [507] 0.9714286 15
## [508] 1.2981818 21
## [509] 1.2795699 21
## [510] 1.5300000 18
## [511] 0.9714286 20
## [512] 0.9633333 17
## [513] 1.3131034 28
## [514] 1.3958944 28
## [515] 1.0052174 17
## [516] 1.2628571 26
## [517] 1.2580645 26
## [518] 1.0336000 19
## [519] 0.9938462 19
## [520] 1.0577778 21
## [521] 1.3161290 30
## [522] 1.2988115 30
## [523] 1.0967742 25
## [524] 1.0317241 22
## [525] 1.3600000 33
## [526] 1.3572581 33
## [527] 1.3096296 26
## [528] 1.1560593 26
## [529] 1.3222222 35
## [530] 1.4395161 35
## [531] 1.3600000 11
## [532] 1.6451613 11
## [533] 2.3974359 11
## [534] 1.3600000 11
## [535] 1.3920596 11
## [536] 2.3571429 11
## [537] 1.3600000 12
## [538] 1.4101382 12
## [539] 1.7435897 12
## [540] 1.3600000 11
## [541] 1.3920596 11
## [542] 1.3600000 11
## [543] 1.5080645 11
## [544] 1.3600000 11
## [545] 1.3920596 11
## [546] 1.3600000 12
## [547] 1.4101382 12
## [548] 1.6320000 12
## [549] 1.3600000 11
## [550] 1.2064516 11
## [551] 1.3600000 11
## [552] 1.2926267 11
## [553] 1.3600000 11
## [554] 1.5080645 11
## [555] 1.2466667 11
## [556] 1.5080645 11
## [557] 1.3600000 11
## [558] 1.6451613 11
## [559] 1.3600000 11
## [560] 1.5080645 11
## [561] 1.3600000 11
## [562] 1.5080645 11
## [563] 1.3600000 11
## [564] 1.3920596 11
## [565] 1.3600000 15
## [566] 1.5423387 15
## [567] 1.4285714 15
## [568] 1.3600000 12
## [569] 1.6451613 12
## [570] 1.3600000 11
## [571] 1.3920596 11
## [572] 1.3600000 12
## [573] 1.5186104 12
## [574] 1.3600000 12
## [575] 1.6451613 12
## [576] 1.3600000 14
## [577] 1.5354839 14
## [578] 1.4000000 14
## [579] 1.1507692 11
## [580] 1.6923077 11
## [581] 2.0000000 11
## [582] 1.5583333 11
## [583] 1.0200000 12
## [584] 1.6000000 12
## [585] 1.3600000 12
## [586] 1.3161290 12
## [587] 1.0880000 12
## [588] 2.0000000 12
## [589] 1.3600000 14
## [590] 1.2795699 14
## [591] 1.5555556 14
## [592] 1.3600000 13
## [593] 1.5000000 13
## [594] 1.7333333 13
## [595] 1.3600000 14
## [596] 1.3548387 14
## [597] 1.4736842 14
## [598] 1.2466667 11
## [599] 1.2064516 11
## [600] 1.2466667 11
## [601] 1.6451613 11
## [602] 1.2553846 12
## [603] 1.4101382 12
## [604] 1.0880000 12
## [605] 1.4711538 12
## [606] 1.8461538 12
## [607] 1.3600000 13
## [608] 1.3366935 13
## [609] 1.4444444 13
## [610] 1.3600000 12
## [611] 1.3600000 13
## [612] 1.2466667 11
## [613] 1.5714286 11
## [614] 1.3600000 15
## [615] 1.2988115 15
## [616] 1.5000000 15
## [617] 1.3600000 12
## [618] 1.4101382 12
## [619] 1.3600000 11
## [620] 2.0549451 11
## [621] 2.4285714 11
## [622] 1.3600000 11
## [623] 2.0549451 11
## [624] 1.3600000 11
## [625] 1.2466667 11
## [626] 1.2926267 11
## [627] 1.3600000 11
## [628] 1.3920596 11
## [629] 1.1507692 11
## [630] 1.3920596 11
## [631] 1.3600000 11
## [632] 1.3920596 11
## [633] 1.2693333 14
## [634] 1.4395161 14
## [635] 1.3600000 13
## [636] 1.3366935 13
## [637] 1.3600000 11
## [638] 1.2926267 11
## [639] 1.3600000 11
## [640] 1.3920596 11
## [641] 1.3600000 11
## [642] 1.3920596 11
## [643] 1.3600000 12
## [644] 1.3161290 12
## [645] 1.3600000 16
## [646] 1.4623656 16
## [647] 1.3737374 16
## [648] 1.3600000 12
## [649] 1.5186104 12
## [650] 1.3600000 11
## [651] 1.2926267 11
## [652] 1.0685714 11
## [653] 1.4666667 11
## [654] 1.3600000 13
## [655] 1.2580645 13
## [656] 1.3600000 11
## [657] 1.9179487 11
## [658] 1.3600000 12
## [659] 1.9615385 12
## [660] 1.3600000 13
## [661] 1.3600000 11
## [662] 1.2064516 11
## [663] 1.3600000 12
## [664] 1.2338710 12
## [665] 1.3600000 13
## [666] 1.4258065 13
## [667] 1.3600000 12
## [668] 1.4101382 12
## [669] 1.3600000 11
## [670] 1.3920596 11
## [671] 1.3600000 12
## [672] 1.3161290 12
## [673] 1.3600000 12
## [674] 1.4101382 12
## [675] 1.3600000 12
## [676] 1.5186104 12
## [677] 1.3600000 13
## [678] 1.3366935 13
## [679] 1.3600000 12
## [680] 1.2338710 12
## [681] 1.3600000 13
## [682] 1.3366935 13
## [683] 1.3600000 14
## [684] 1.4395161 14
## [685] 1.3600000 16
## [686] 1.3853990 16
## [687] 1.3600000 11
## [688] 1.3600000 14
## [689] 1.5354839 14
## [690] 1.3600000 12
## [691] 1.3161290 12
## [692] 1.3600000 11
## [693] 1.2926267 11
## [694] 1.3600000 12
## [695] 1.4400000 12
## [696] 1.3600000 14
## [697] 1.3548387 14
## [698] 1.3600000 12
## [699] 1.3600000 11
## [700] 1.3600000 15
## [701] 1.3600000 12
## [702] 1.3600000 11
## [703] 1.3600000 14
## [704] 1.1516129 14
## [705] 1.3600000 13
## [706] 1.1881720 13
## [707] 1.3600000 14
## [708] 1.2795699 14
## [709] 1.2466667 11
## [710] 1.3920596 11
## [711] 1.3851852 11
## [712] 1.4960000 11
## [713] 1.2553846 12
## [714] 1.2338710 12
## [715] 1.4841270 11
## [716] 1.4384615 11
## [717] 1.5583333 11
## [718] 1.3600000 13
## [719] 1.4258065 13
## [720] 1.2628571 13
## [721] 1.3366935 13
## [722] 1.6696429 11
## [723] 1.4960000 11
## [724] 1.5300000 12
## [725] 1.8214286 12
## [726] 0.9822222 13
## [727] 1.5600000 13
## [728] 1.5347222 13
## [729] 1.2844444 17
## [730] 1.3983871 17
## [731] 1.3851852 11
## [732] 1.6597633 11
## [733] 1.5583333 11
## [734] 1.2750000 15
## [735] 1.4516129 15
## [736] 1.3600000 14
## [737] 1.3548387 14
## [738] 1.2628571 13
## [739] 1.2580645 13
## [740] 1.2693333 14
## [741] 1.5354839 14
## [742] 1.5412088 11
## [743] 0.9600000 12
## [744] 1.2628571 13
## [745] 1.1881720 13
## [746] 1.0685714 11
## [747] 1.3600000 13
## [748] 1.2580645 13
## [749] 1.2466667 11
## [750] 1.2800000 16
## [751] 1.4623656 16
## [752] 1.4166667 12
## [753] 1.5692308 12
## [754] 1.0200000 12
## [755] 0.9973333 11
## [756] 1.3600000 16
## [757] 1.3853990 16
## [758] 1.4841270 11
## [759] 0.9822222 13
## [760] 1.2628571 13
## [761] 1.2844444 17
## [762] 1.3317972 17
## [763] 1.3641975 13
## [764] 1.5294118 13
## [765] 1.0880000 16
## [766] 1.4265734 16
## [767] 1.6000000 16
## [768] 1.3600000 18
## [769] 1.1845161 18
## [770] 1.3600000 19
## [771] 1.4208211 19
## [772] 1.2800000 16
## [773] 1.3161290 16
## [774] 1.2466667 11
## [775] 1.3920596 11
## [776] 1.4960000 11
## [777] 1.7980769 11
## [778] 1.3600000 13
## [779] 1.3366935 13
## [780] 1.3641975 13
## [781] 1.6250000 13## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 23
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[3228 item(s), 237 transaction(s)] done [0.00s].
## sorting and recoding items ... [118 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 done [0.00s].
## writing ... [667 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
## lhs rhs support
## [1] {layer} => {network} 0.1181435
## [2] {estim} => {network} 0.1054852
## [3] {implement} => {network} 0.1054852
## [4] {success} => {network} 0.1054852
## [5] {experiment} => {result} 0.1139241
## [6] {experiment} => {network} 0.1012658
## [7] {increas} => {network} 0.1012658
## [8] {input} => {network} 0.1223629
## [9] {research} => {data} 0.1054852
## [10] {order} => {network} 0.1265823
## [11] {studi} => {network} 0.1054852
## [12] {cnn} => {convolut} 0.1350211
## [13] {cnn} => {neural} 0.1308017
## [14] {cnn} => {network} 0.1392405
## [15] {optim} => {network} 0.1097046
## [16] {time} => {network} 0.1223629
## [17] {complex} => {network} 0.1181435
## [18] {structur} => {network} 0.1223629
## [19] {outperform} => {network} 0.1308017
## [20] {human} => {network} 0.1181435
## [21] {effici} => {neural} 0.1223629
## [22] {effici} => {network} 0.1518987
## [23] {appli} => {network} 0.1392405
## [24] {techniqu} => {network} 0.1223629
## [25] {test} => {network} 0.1392405
## [26] {object} => {neural} 0.1392405
## [27] {object} => {network} 0.1476793
## [28] {multipl} => {network} 0.1350211
## [29] {process} => {network} 0.1350211
## [30] {experi} => {network} 0.1518987
## [31] {set} => {network} 0.1518987
## [32] {recent} => {network} 0.1434599
## [33] {improv} => {network} 0.1518987
## [34] {effect} => {network} 0.1476793
## [35] {predict} => {network} 0.1476793
## [36] {requir} => {network} 0.1392405
## [37] {present} => {network} 0.1561181
## [38] {learn} => {network} 0.1687764
## [39] {compar} => {network} 0.1772152
## [40] {develop} => {model} 0.1561181
## [41] {machin} => {neural} 0.1518987
## [42] {machin} => {network} 0.1603376
## [43] {framework} => {network} 0.1561181
## [44] {accuraci} => {network} 0.1645570
## [45] {classif} => {network} 0.2067511
## [46] {challeng} => {network} 0.1603376
## [47] {work} => {network} 0.1729958
## [48] {architectur} => {network} 0.2236287
## [49] {stateoftheart} => {network} 0.1898734
## [50] {applic} => {network} 0.1940928
## [51] {problem} => {network} 0.2109705
## [52] {demonstr} => {network} 0.2025316
## [53] {comput} => {network} 0.2067511
## [54] {achiev} => {network} 0.2194093
## [55] {featur} => {network} 0.2489451
## [56] {base} => {network} 0.2362869
## [57] {show} => {network} 0.2827004
## [58] {paper} => {network} 0.3248945
## [59] {method} => {network} 0.3037975
## [60] {convolut} => {neural} 0.3459916
## [61] {convolut} => {network} 0.3797468
## [62] {imag} => {network} 0.3291139
## [63] {approach} => {network} 0.3122363
## [64] {perform} => {network} 0.3122363
## [65] {propos} => {network} 0.3333333
## [66] {result} => {network} 0.3544304
## [67] {data} => {network} 0.3333333
## [68] {train} => {network} 0.3628692
## [69] {neural} => {network} 0.5738397
## [70] {network} => {neural} 0.5738397
## [71] {cnn,convolut} => {neural} 0.1308017
## [72] {neural,cnn} => {convolut} 0.1308017
## [73] {cnn,convolut} => {network} 0.1350211
## [74] {network,cnn} => {convolut} 0.1350211
## [75] {neural,cnn} => {network} 0.1308017
## [76] {network,cnn} => {neural} 0.1308017
## [77] {neural,recognit} => {network} 0.1054852
## [78] {network,recognit} => {neural} 0.1054852
## [79] {neural,outperform} => {network} 0.1054852
## [80] {network,outperform} => {neural} 0.1054852
## [81] {effici,neural} => {network} 0.1223629
## [82] {effici,network} => {neural} 0.1223629
## [83] {neural,appli} => {network} 0.1012658
## [84] {network,appli} => {neural} 0.1012658
## [85] {neural,techniqu} => {network} 0.1097046
## [86] {network,techniqu} => {neural} 0.1097046
## [87] {neural,test} => {network} 0.1223629
## [88] {network,test} => {neural} 0.1223629
## [89] {detect,neural} => {network} 0.1012658
## [90] {detect,network} => {neural} 0.1012658
## [91] {specif,neural} => {network} 0.1054852
## [92] {specif,network} => {neural} 0.1054852
## [93] {imag,object} => {neural} 0.1012658
## [94] {neural,object} => {imag} 0.1012658
## [95] {imag,object} => {network} 0.1012658
## [96] {neural,object} => {network} 0.1350211
## [97] {network,object} => {neural} 0.1350211
## [98] {multipl,neural} => {network} 0.1139241
## [99] {network,multipl} => {neural} 0.1139241
## [100] {neural,process} => {network} 0.1139241
## [101] {network,process} => {neural} 0.1139241
## [102] {experi,neural} => {network} 0.1350211
## [103] {network,experi} => {neural} 0.1350211
## [104] {set,neural} => {network} 0.1139241
## [105] {network,set} => {neural} 0.1139241
## [106] {neural,recent} => {network} 0.1223629
## [107] {network,recent} => {neural} 0.1223629
## [108] {improv,perform} => {network} 0.1097046
## [109] {improv,network} => {perform} 0.1097046
## [110] {improv,neural} => {network} 0.1223629
## [111] {improv,network} => {neural} 0.1223629
## [112] {effect,neural} => {network} 0.1181435
## [113] {network,effect} => {neural} 0.1181435
## [114] {neural,predict} => {network} 0.1223629
## [115] {network,predict} => {neural} 0.1223629
## [116] {neural,larg} => {network} 0.1139241
## [117] {network,larg} => {neural} 0.1139241
## [118] {requir,neural} => {network} 0.1223629
## [119] {network,requir} => {neural} 0.1223629
## [120] {neural,present} => {network} 0.1139241
## [121] {network,present} => {neural} 0.1139241
## [122] {learn,neural} => {network} 0.1350211
## [123] {learn,network} => {neural} 0.1350211
## [124] {result,compar} => {network} 0.1139241
## [125] {neural,compar} => {network} 0.1308017
## [126] {network,compar} => {neural} 0.1308017
## [127] {neural,develop} => {network} 0.1265823
## [128] {network,develop} => {neural} 0.1265823
## [129] {neural,machin} => {network} 0.1476793
## [130] {network,machin} => {neural} 0.1476793
## [131] {propos,framework} => {network} 0.1054852
## [132] {neural,framework} => {network} 0.1223629
## [133] {network,framework} => {neural} 0.1223629
## [134] {model,accuraci} => {network} 0.1054852
## [135] {neural,accuraci} => {network} 0.1265823
## [136] {network,accuraci} => {neural} 0.1265823
## [137] {classif,paper} => {network} 0.1097046
## [138] {classif,convolut} => {neural} 0.1097046
## [139] {classif,convolut} => {network} 0.1139241
## [140] {imag,classif} => {network} 0.1054852
## [141] {propos,classif} => {network} 0.1012658
## [142] {classif,data} => {network} 0.1054852
## [143] {train,classif} => {neural} 0.1012658
## [144] {train,classif} => {network} 0.1139241
## [145] {classif,neural} => {network} 0.1729958
## [146] {classif,network} => {neural} 0.1729958
## [147] {challeng,convolut} => {network} 0.1012658
## [148] {challeng,neural} => {network} 0.1350211
## [149] {challeng,network} => {neural} 0.1350211
## [150] {work,neural} => {network} 0.1434599
## [151] {work,network} => {neural} 0.1434599
## [152] {task,convolut} => {network} 0.1097046
## [153] {propos,task} => {network} 0.1097046
## [154] {neural,task} => {network} 0.1476793
## [155] {network,task} => {neural} 0.1476793
## [156] {architectur,paper} => {network} 0.1097046
## [157] {architectur,convolut} => {neural} 0.1054852
## [158] {architectur,convolut} => {network} 0.1265823
## [159] {imag,architectur} => {network} 0.1012658
## [160] {approach,architectur} => {network} 0.1139241
## [161] {propos,architectur} => {network} 0.1012658
## [162] {result,architectur} => {network} 0.1054852
## [163] {train,architectur} => {network} 0.1054852
## [164] {architectur,neural} => {network} 0.1603376
## [165] {network,architectur} => {neural} 0.1603376
## [166] {stateoftheart,convolut} => {network} 0.1054852
## [167] {imag,stateoftheart} => {network} 0.1054852
## [168] {stateoftheart,approach} => {network} 0.1054852
## [169] {propos,stateoftheart} => {network} 0.1139241
## [170] {result,stateoftheart} => {network} 0.1265823
## [171] {stateoftheart,data} => {network} 0.1012658
## [172] {stateoftheart,neural} => {network} 0.1518987
## [173] {stateoftheart,network} => {neural} 0.1518987
## [174] {perform,applic} => {network} 0.1054852
## [175] {model,applic} => {network} 0.1012658
## [176] {neural,applic} => {network} 0.1561181
## [177] {network,applic} => {neural} 0.1561181
## [178] {neural,system} => {network} 0.1350211
## [179] {network,system} => {neural} 0.1350211
## [180] {paper,problem} => {neural} 0.1097046
## [181] {paper,problem} => {network} 0.1265823
## [182] {convolut,problem} => {neural} 0.1012658
## [183] {convolut,problem} => {network} 0.1054852
## [184] {approach,problem} => {propos} 0.1054852
## [185] {approach,problem} => {neural} 0.1012658
## [186] {approach,problem} => {network} 0.1181435
## [187] {propos,problem} => {network} 0.1181435
## [188] {result,problem} => {network} 0.1308017
## [189] {neural,problem} => {network} 0.1729958
## [190] {network,problem} => {neural} 0.1729958
## [191] {algorithm,train} => {network} 0.1139241
## [192] {algorithm,neural} => {network} 0.1350211
## [193] {algorithm,network} => {neural} 0.1350211
## [194] {propos,demonstr} => {network} 0.1054852
## [195] {result,demonstr} => {network} 0.1012658
## [196] {train,demonstr} => {neural} 0.1223629
## [197] {neural,demonstr} => {train} 0.1223629
## [198] {train,demonstr} => {network} 0.1223629
## [199] {neural,demonstr} => {network} 0.1603376
## [200] {network,demonstr} => {neural} 0.1603376
## [201] {convolut,comput} => {neural} 0.1139241
## [202] {convolut,comput} => {network} 0.1097046
## [203] {imag,comput} => {neural} 0.1012658
## [204] {imag,comput} => {network} 0.1054852
## [205] {propos,comput} => {neural} 0.1012658
## [206] {propos,comput} => {network} 0.1181435
## [207] {result,comput} => {network} 0.1054852
## [208] {data,comput} => {network} 0.1012658
## [209] {train,comput} => {neural} 0.1097046
## [210] {train,comput} => {network} 0.1223629
## [211] {neural,comput} => {network} 0.1856540
## [212] {network,comput} => {neural} 0.1856540
## [213] {achiev,convolut} => {neural} 0.1097046
## [214] {achiev,convolut} => {network} 0.1265823
## [215] {imag,achiev} => {network} 0.1265823
## [216] {achiev,approach} => {network} 0.1308017
## [217] {perform,achiev} => {network} 0.1097046
## [218] {propos,achiev} => {network} 0.1139241
## [219] {result,achiev} => {network} 0.1265823
## [220] {data,achiev} => {network} 0.1012658
## [221] {train,achiev} => {network} 0.1139241
## [222] {achiev,neural} => {network} 0.1772152
## [223] {network,achiev} => {neural} 0.1772152
## [224] {show,featur} => {result} 0.1181435
## [225] {show,featur} => {neural} 0.1097046
## [226] {show,featur} => {network} 0.1181435
## [227] {featur,paper} => {network} 0.1350211
## [228] {method,featur} => {neural} 0.1097046
## [229] {method,featur} => {network} 0.1223629
## [230] {featur,convolut} => {neural} 0.1392405
## [231] {featur,convolut} => {network} 0.1518987
## [232] {imag,featur} => {neural} 0.1012658
## [233] {imag,featur} => {network} 0.1139241
## [234] {featur,approach} => {network} 0.1265823
## [235] {perform,featur} => {network} 0.1139241
## [236] {propos,featur} => {network} 0.1518987
## [237] {result,featur} => {network} 0.1350211
## [238] {data,featur} => {network} 0.1265823
## [239] {train,featur} => {neural} 0.1012658
## [240] {train,featur} => {network} 0.1054852
## [241] {featur,neural} => {network} 0.2109705
## [242] {featur,network} => {neural} 0.2109705
## [243] {method,base} => {network} 0.1054852
## [244] {base,convolut} => {neural} 0.1308017
## [245] {base,neural} => {convolut} 0.1308017
## [246] {base,convolut} => {network} 0.1350211
## [247] {imag,base} => {neural} 0.1054852
## [248] {imag,base} => {network} 0.1097046
## [249] {base,approach} => {network} 0.1265823
## [250] {propos,base} => {network} 0.1097046
## [251] {result,base} => {network} 0.1223629
## [252] {base,data} => {network} 0.1181435
## [253] {train,base} => {network} 0.1223629
## [254] {base,neural} => {network} 0.1772152
## [255] {base,network} => {neural} 0.1772152
## [256] {show,paper} => {network} 0.1181435
## [257] {method,show} => {network} 0.1223629
## [258] {show,convolut} => {result} 0.1223629
## [259] {show,convolut} => {neural} 0.1518987
## [260] {show,convolut} => {network} 0.1645570
## [261] {imag,show} => {network} 0.1350211
## [262] {show,approach} => {network} 0.1350211
## [263] {perform,show} => {neural} 0.1139241
## [264] {perform,show} => {network} 0.1350211
## [265] {propos,show} => {network} 0.1392405
## [266] {result,show} => {network} 0.1814346
## [267] {show,data} => {network} 0.1645570
## [268] {show,train} => {network} 0.1308017
## [269] {show,neural} => {network} 0.2362869
## [270] {show,network} => {neural} 0.2362869
## [271] {dataset,convolut} => {neural} 0.1139241
## [272] {dataset,convolut} => {network} 0.1223629
## [273] {dataset,imag} => {network} 0.1350211
## [274] {dataset,approach} => {network} 0.1265823
## [275] {dataset,result} => {network} 0.1350211
## [276] {dataset,neural} => {network} 0.1898734
## [277] {dataset,network} => {neural} 0.1898734
## [278] {method,paper} => {network} 0.1561181
## [279] {paper,convolut} => {neural} 0.1518987
## [280] {paper,convolut} => {network} 0.1645570
## [281] {imag,paper} => {neural} 0.1265823
## [282] {imag,paper} => {network} 0.1518987
## [283] {approach,paper} => {network} 0.1561181
## [284] {perform,paper} => {network} 0.1308017
## [285] {propos,paper} => {network} 0.1898734
## [286] {result,paper} => {network} 0.1603376
## [287] {data,paper} => {neural} 0.1350211
## [288] {data,paper} => {network} 0.1687764
## [289] {train,paper} => {neural} 0.1392405
## [290] {train,paper} => {network} 0.1645570
## [291] {model,paper} => {neural} 0.1350211
## [292] {model,paper} => {network} 0.1350211
## [293] {neural,paper} => {network} 0.2616034
## [294] {network,paper} => {neural} 0.2616034
## [295] {method,convolut} => {imag} 0.1518987
## [296] {method,convolut} => {neural} 0.1814346
## [297] {method,neural} => {convolut} 0.1814346
## [298] {method,convolut} => {network} 0.2025316
## [299] {imag,method} => {neural} 0.1729958
## [300] {imag,method} => {network} 0.1940928
## [301] {method,approach} => {neural} 0.1265823
## [302] {method,approach} => {network} 0.1392405
## [303] {method,perform} => {network} 0.1181435
## [304] {method,propos} => {network} 0.1476793
## [305] {method,result} => {network} 0.1561181
## [306] {method,data} => {neural} 0.1392405
## [307] {method,data} => {network} 0.1561181
## [308] {method,train} => {network} 0.1687764
## [309] {method,neural} => {network} 0.2531646
## [310] {method,network} => {neural} 0.2531646
## [311] {imag,convolut} => {neural} 0.2067511
## [312] {imag,neural} => {convolut} 0.2067511
## [313] {imag,convolut} => {network} 0.2236287
## [314] {approach,convolut} => {neural} 0.1940928
## [315] {approach,neural} => {convolut} 0.1940928
## [316] {approach,convolut} => {network} 0.2067511
## [317] {perform,convolut} => {neural} 0.1518987
## [318] {perform,convolut} => {network} 0.1772152
## [319] {propos,convolut} => {neural} 0.1561181
## [320] {propos,convolut} => {network} 0.1772152
## [321] {result,convolut} => {neural} 0.1687764
## [322] {result,convolut} => {network} 0.1940928
## [323] {data,convolut} => {neural} 0.1645570
## [324] {data,convolut} => {network} 0.1772152
## [325] {train,convolut} => {neural} 0.1603376
## [326] {train,convolut} => {network} 0.1729958
## [327] {model,convolut} => {neural} 0.1645570
## [328] {model,convolut} => {network} 0.1603376
## [329] {neural,convolut} => {network} 0.3375527
## [330] {network,convolut} => {neural} 0.3375527
## [331] {imag,approach} => {network} 0.1518987
## [332] {imag,perform} => {network} 0.1561181
## [333] {imag,propos} => {network} 0.1603376
## [334] {imag,result} => {network} 0.1561181
## [335] {imag,data} => {neural} 0.1518987
## [336] {imag,data} => {network} 0.1645570
## [337] {imag,train} => {neural} 0.1518987
## [338] {imag,train} => {network} 0.1687764
## [339] {imag,neural} => {network} 0.2742616
## [340] {imag,network} => {neural} 0.2742616
## [341] {perform,approach} => {propos} 0.1265823
## [342] {perform,approach} => {network} 0.1392405
## [343] {propos,approach} => {network} 0.1814346
## [344] {result,approach} => {network} 0.1603376
## [345] {data,approach} => {network} 0.1772152
## [346] {train,approach} => {network} 0.1434599
## [347] {model,approach} => {network} 0.1518987
## [348] {approach,neural} => {network} 0.2489451
## [349] {network,approach} => {neural} 0.2489451
## [350] {perform,propos} => {network} 0.1687764
## [351] {perform,result} => {network} 0.1350211
## [352] {perform,data} => {neural} 0.1308017
## [353] {perform,data} => {network} 0.1561181
## [354] {perform,train} => {network} 0.1645570
## [355] {perform,neural} => {network} 0.2447257
## [356] {perform,network} => {neural} 0.2447257
## [357] {propos,result} => {network} 0.1645570
## [358] {propos,data} => {network} 0.1645570
## [359] {propos,train} => {network} 0.1645570
## [360] {propos,neural} => {network} 0.2489451
## [361] {propos,network} => {neural} 0.2489451
## [362] {result,data} => {network} 0.1687764
## [363] {result,train} => {network} 0.1603376
## [364] {model,result} => {network} 0.1476793
## [365] {result,neural} => {network} 0.2742616
## [366] {result,network} => {neural} 0.2742616
## [367] {train,data} => {network} 0.1856540
## [368] {data,neural} => {network} 0.2742616
## [369] {data,network} => {neural} 0.2742616
## [370] {train,neural} => {network} 0.2827004
## [371] {train,network} => {neural} 0.2827004
## [372] {model,neural} => {network} 0.2827004
## [373] {model,network} => {neural} 0.2827004
## [374] {neural,cnn,convolut} => {network} 0.1308017
## [375] {network,cnn,convolut} => {neural} 0.1308017
## [376] {network,neural,cnn} => {convolut} 0.1308017
## [377] {classif,neural,convolut} => {network} 0.1097046
## [378] {classif,network,convolut} => {neural} 0.1097046
## [379] {train,classif,neural} => {network} 0.1012658
## [380] {train,classif,network} => {neural} 0.1012658
## [381] {architectur,neural,convolut} => {network} 0.1054852
## [382] {network,architectur,convolut} => {neural} 0.1054852
## [383] {neural,paper,problem} => {network} 0.1054852
## [384] {network,paper,problem} => {neural} 0.1054852
## [385] {approach,neural,problem} => {network} 0.1012658
## [386] {network,approach,problem} => {neural} 0.1012658
## [387] {train,neural,demonstr} => {network} 0.1097046
## [388] {train,network,demonstr} => {neural} 0.1097046
## [389] {neural,convolut,comput} => {network} 0.1097046
## [390] {network,convolut,comput} => {neural} 0.1097046
## [391] {propos,neural,comput} => {network} 0.1012658
## [392] {propos,network,comput} => {neural} 0.1012658
## [393] {train,neural,comput} => {network} 0.1054852
## [394] {train,network,comput} => {neural} 0.1054852
## [395] {achiev,neural,convolut} => {network} 0.1054852
## [396] {network,achiev,convolut} => {neural} 0.1054852
## [397] {achiev,approach,neural} => {network} 0.1054852
## [398] {network,achiev,approach} => {neural} 0.1054852
## [399] {show,featur,neural} => {network} 0.1097046
## [400] {show,featur,network} => {neural} 0.1097046
## [401] {featur,neural,paper} => {network} 0.1097046
## [402] {featur,network,paper} => {neural} 0.1097046
## [403] {method,featur,neural} => {network} 0.1097046
## [404] {method,featur,network} => {neural} 0.1097046
## [405] {featur,neural,convolut} => {network} 0.1392405
## [406] {featur,network,convolut} => {neural} 0.1392405
## [407] {imag,featur,neural} => {network} 0.1012658
## [408] {imag,featur,network} => {neural} 0.1012658
## [409] {featur,approach,neural} => {network} 0.1097046
## [410] {featur,network,approach} => {neural} 0.1097046
## [411] {propos,featur,neural} => {network} 0.1139241
## [412] {propos,featur,network} => {neural} 0.1139241
## [413] {result,featur,neural} => {network} 0.1139241
## [414] {result,featur,network} => {neural} 0.1139241
## [415] {data,featur,neural} => {network} 0.1012658
## [416] {data,featur,network} => {neural} 0.1012658
## [417] {train,featur,neural} => {network} 0.1012658
## [418] {train,featur,network} => {neural} 0.1012658
## [419] {base,neural,convolut} => {network} 0.1265823
## [420] {base,network,convolut} => {neural} 0.1265823
## [421] {base,network,neural} => {convolut} 0.1265823
## [422] {imag,base,neural} => {network} 0.1012658
## [423] {imag,base,network} => {neural} 0.1012658
## [424] {show,neural,paper} => {network} 0.1012658
## [425] {show,network,paper} => {neural} 0.1012658
## [426] {method,show,neural} => {network} 0.1054852
## [427] {method,show,network} => {neural} 0.1054852
## [428] {imag,show,convolut} => {neural} 0.1012658
## [429] {imag,show,neural} => {convolut} 0.1012658
## [430] {imag,show,convolut} => {network} 0.1012658
## [431] {imag,show,network} => {convolut} 0.1012658
## [432] {result,show,convolut} => {neural} 0.1054852
## [433] {result,show,convolut} => {network} 0.1139241
## [434] {show,neural,convolut} => {network} 0.1476793
## [435] {show,network,convolut} => {neural} 0.1476793
## [436] {imag,show,neural} => {network} 0.1181435
## [437] {imag,show,network} => {neural} 0.1181435
## [438] {show,approach,neural} => {network} 0.1097046
## [439] {show,network,approach} => {neural} 0.1097046
## [440] {perform,show,neural} => {network} 0.1139241
## [441] {perform,show,network} => {neural} 0.1139241
## [442] {propos,show,neural} => {network} 0.1139241
## [443] {propos,show,network} => {neural} 0.1139241
## [444] {result,show,data} => {network} 0.1012658
## [445] {result,show,neural} => {network} 0.1518987
## [446] {result,show,network} => {neural} 0.1518987
## [447] {show,data,neural} => {network} 0.1350211
## [448] {show,data,network} => {neural} 0.1350211
## [449] {show,train,neural} => {network} 0.1012658
## [450] {show,train,network} => {neural} 0.1012658
## [451] {dataset,method,neural} => {network} 0.1012658
## [452] {dataset,method,network} => {neural} 0.1012658
## [453] {dataset,neural,convolut} => {network} 0.1097046
## [454] {dataset,network,convolut} => {neural} 0.1097046
## [455] {dataset,imag,neural} => {network} 0.1097046
## [456] {dataset,imag,network} => {neural} 0.1097046
## [457] {dataset,result,neural} => {network} 0.1012658
## [458] {dataset,result,network} => {neural} 0.1012658
## [459] {dataset,data,neural} => {network} 0.1054852
## [460] {dataset,data,network} => {neural} 0.1054852
## [461] {dataset,train,neural} => {network} 0.1181435
## [462] {dataset,train,network} => {neural} 0.1181435
## [463] {dataset,model,neural} => {network} 0.1012658
## [464] {dataset,model,network} => {neural} 0.1012658
## [465] {method,neural,paper} => {network} 0.1223629
## [466] {method,network,paper} => {neural} 0.1223629
## [467] {approach,paper,convolut} => {network} 0.1054852
## [468] {propos,paper,convolut} => {network} 0.1054852
## [469] {neural,paper,convolut} => {network} 0.1476793
## [470] {network,paper,convolut} => {neural} 0.1476793
## [471] {imag,propos,paper} => {network} 0.1054852
## [472] {imag,neural,paper} => {network} 0.1223629
## [473] {imag,network,paper} => {neural} 0.1223629
## [474] {propos,approach,paper} => {network} 0.1012658
## [475] {approach,neural,paper} => {network} 0.1181435
## [476] {network,approach,paper} => {neural} 0.1181435
## [477] {perform,neural,paper} => {network} 0.1054852
## [478] {perform,network,paper} => {neural} 0.1054852
## [479] {propos,result,paper} => {network} 0.1054852
## [480] {propos,neural,paper} => {network} 0.1392405
## [481] {propos,network,paper} => {neural} 0.1392405
## [482] {result,neural,paper} => {network} 0.1223629
## [483] {result,network,paper} => {neural} 0.1223629
## [484] {data,neural,paper} => {network} 0.1350211
## [485] {data,network,paper} => {neural} 0.1350211
## [486] {train,neural,paper} => {network} 0.1350211
## [487] {train,network,paper} => {neural} 0.1350211
## [488] {model,neural,paper} => {network} 0.1265823
## [489] {model,network,paper} => {neural} 0.1265823
## [490] {imag,method,convolut} => {neural} 0.1392405
## [491] {method,neural,convolut} => {imag} 0.1392405
## [492] {imag,method,neural} => {convolut} 0.1392405
## [493] {imag,method,convolut} => {network} 0.1518987
## [494] {method,network,convolut} => {imag} 0.1518987
## [495] {imag,method,network} => {convolut} 0.1518987
## [496] {method,approach,convolut} => {neural} 0.1012658
## [497] {method,approach,neural} => {convolut} 0.1012658
## [498] {method,approach,convolut} => {network} 0.1097046
## [499] {method,network,approach} => {convolut} 0.1097046
## [500] {method,propos,convolut} => {network} 0.1012658
## [501] {method,result,convolut} => {network} 0.1097046
## [502] {method,result,network} => {convolut} 0.1097046
## [503] {method,data,convolut} => {neural} 0.1097046
## [504] {method,data,neural} => {convolut} 0.1097046
## [505] {method,data,convolut} => {network} 0.1181435
## [506] {method,data,network} => {convolut} 0.1181435
## [507] {method,neural,convolut} => {network} 0.1814346
## [508] {method,network,convolut} => {neural} 0.1814346
## [509] {method,network,neural} => {convolut} 0.1814346
## [510] {imag,method,result} => {network} 0.1012658
## [511] {imag,method,data} => {neural} 0.1012658
## [512] {method,data,neural} => {imag} 0.1012658
## [513] {imag,method,data} => {network} 0.1097046
## [514] {method,data,network} => {imag} 0.1097046
## [515] {imag,method,train} => {neural} 0.1012658
## [516] {method,train,neural} => {imag} 0.1012658
## [517] {imag,method,train} => {network} 0.1139241
## [518] {imag,method,neural} => {network} 0.1729958
## [519] {imag,method,network} => {neural} 0.1729958
## [520] {method,approach,neural} => {network} 0.1223629
## [521] {method,network,approach} => {neural} 0.1223629
## [522] {method,propos,neural} => {network} 0.1181435
## [523] {method,propos,network} => {neural} 0.1181435
## [524] {method,result,neural} => {network} 0.1181435
## [525] {method,result,network} => {neural} 0.1181435
## [526] {method,data,neural} => {network} 0.1350211
## [527] {method,data,network} => {neural} 0.1350211
## [528] {method,train,neural} => {network} 0.1350211
## [529] {method,train,network} => {neural} 0.1350211
## [530] {method,model,neural} => {network} 0.1139241
## [531] {method,model,network} => {neural} 0.1139241
## [532] {imag,approach,convolut} => {neural} 0.1181435
## [533] {imag,approach,neural} => {convolut} 0.1181435
## [534] {imag,approach,convolut} => {network} 0.1223629
## [535] {imag,network,approach} => {convolut} 0.1223629
## [536] {imag,perform,convolut} => {network} 0.1139241
## [537] {imag,perform,network} => {convolut} 0.1139241
## [538] {imag,propos,convolut} => {network} 0.1054852
## [539] {imag,result,convolut} => {network} 0.1139241
## [540] {imag,result,network} => {convolut} 0.1139241
## [541] {imag,data,convolut} => {neural} 0.1097046
## [542] {imag,data,neural} => {convolut} 0.1097046
## [543] {imag,data,convolut} => {network} 0.1139241
## [544] {imag,train,convolut} => {neural} 0.1054852
## [545] {imag,train,convolut} => {network} 0.1054852
## [546] {imag,neural,convolut} => {network} 0.1983122
## [547] {imag,network,convolut} => {neural} 0.1983122
## [548] {imag,network,neural} => {convolut} 0.1983122
## [549] {perform,approach,convolut} => {network} 0.1012658
## [550] {perform,network,approach} => {convolut} 0.1012658
## [551] {propos,approach,convolut} => {neural} 0.1054852
## [552] {propos,approach,neural} => {convolut} 0.1054852
## [553] {propos,approach,convolut} => {network} 0.1139241
## [554] {result,approach,convolut} => {network} 0.1054852
## [555] {data,approach,convolut} => {neural} 0.1097046
## [556] {data,approach,neural} => {convolut} 0.1097046
## [557] {data,approach,convolut} => {network} 0.1223629
## [558] {approach,neural,convolut} => {network} 0.1898734
## [559] {network,approach,convolut} => {neural} 0.1898734
## [560] {network,approach,neural} => {convolut} 0.1898734
## [561] {perform,propos,convolut} => {network} 0.1012658
## [562] {perform,neural,convolut} => {network} 0.1518987
## [563] {perform,network,convolut} => {neural} 0.1518987
## [564] {propos,result,convolut} => {network} 0.1054852
## [565] {propos,neural,convolut} => {network} 0.1518987
## [566] {propos,network,convolut} => {neural} 0.1518987
## [567] {result,neural,convolut} => {network} 0.1645570
## [568] {result,network,convolut} => {neural} 0.1645570
## [569] {train,data,convolut} => {network} 0.1012658
## [570] {data,neural,convolut} => {network} 0.1645570
## [571] {data,network,convolut} => {neural} 0.1645570
## [572] {train,neural,convolut} => {network} 0.1518987
## [573] {train,network,convolut} => {neural} 0.1518987
## [574] {model,neural,convolut} => {network} 0.1603376
## [575] {model,network,convolut} => {neural} 0.1603376
## [576] {imag,approach,neural} => {network} 0.1223629
## [577] {imag,network,approach} => {neural} 0.1223629
## [578] {imag,perform,neural} => {network} 0.1265823
## [579] {imag,perform,network} => {neural} 0.1265823
## [580] {imag,propos,neural} => {network} 0.1223629
## [581] {imag,propos,network} => {neural} 0.1223629
## [582] {imag,result,neural} => {network} 0.1139241
## [583] {imag,result,network} => {neural} 0.1139241
## [584] {imag,data,neural} => {network} 0.1518987
## [585] {imag,data,network} => {neural} 0.1518987
## [586] {imag,train,neural} => {network} 0.1434599
## [587] {imag,train,network} => {neural} 0.1434599
## [588] {imag,model,neural} => {network} 0.1054852
## [589] {imag,model,network} => {neural} 0.1054852
## [590] {perform,propos,approach} => {network} 0.1054852
## [591] {perform,network,approach} => {propos} 0.1054852
## [592] {perform,approach,neural} => {network} 0.1097046
## [593] {perform,network,approach} => {neural} 0.1097046
## [594] {propos,result,approach} => {network} 0.1054852
## [595] {propos,data,approach} => {network} 0.1097046
## [596] {propos,approach,neural} => {network} 0.1350211
## [597] {propos,network,approach} => {neural} 0.1350211
## [598] {result,approach,neural} => {network} 0.1223629
## [599] {result,network,approach} => {neural} 0.1223629
## [600] {data,approach,neural} => {network} 0.1392405
## [601] {data,network,approach} => {neural} 0.1392405
## [602] {train,approach,neural} => {network} 0.1097046
## [603] {train,network,approach} => {neural} 0.1097046
## [604] {model,approach,neural} => {network} 0.1350211
## [605] {model,network,approach} => {neural} 0.1350211
## [606] {perform,propos,neural} => {network} 0.1223629
## [607] {perform,propos,network} => {neural} 0.1223629
## [608] {perform,result,neural} => {network} 0.1097046
## [609] {perform,result,network} => {neural} 0.1097046
## [610] {perform,data,neural} => {network} 0.1308017
## [611] {perform,data,network} => {neural} 0.1308017
## [612] {perform,train,neural} => {network} 0.1265823
## [613] {perform,train,network} => {neural} 0.1265823
## [614] {model,perform,neural} => {network} 0.1139241
## [615] {model,perform,network} => {neural} 0.1139241
## [616] {propos,result,neural} => {network} 0.1223629
## [617] {propos,result,network} => {neural} 0.1223629
## [618] {propos,data,neural} => {network} 0.1265823
## [619] {propos,data,network} => {neural} 0.1265823
## [620] {propos,train,neural} => {network} 0.1265823
## [621] {propos,train,network} => {neural} 0.1265823
## [622] {model,propos,neural} => {network} 0.1350211
## [623] {model,propos,network} => {neural} 0.1350211
## [624] {result,data,neural} => {network} 0.1308017
## [625] {result,data,network} => {neural} 0.1308017
## [626] {result,train,neural} => {network} 0.1265823
## [627] {result,train,network} => {neural} 0.1265823
## [628] {model,result,neural} => {network} 0.1223629
## [629] {model,result,network} => {neural} 0.1223629
## [630] {train,data,neural} => {network} 0.1603376
## [631] {train,data,network} => {neural} 0.1603376
## [632] {model,data,neural} => {network} 0.1350211
## [633] {model,data,network} => {neural} 0.1350211
## [634] {model,train,neural} => {network} 0.1350211
## [635] {model,train,network} => {neural} 0.1350211
## [636] {result,show,neural,convolut} => {network} 0.1012658
## [637] {result,show,network,convolut} => {neural} 0.1012658
## [638] {imag,method,neural,convolut} => {network} 0.1392405
## [639] {imag,method,network,convolut} => {neural} 0.1392405
## [640] {method,network,neural,convolut} => {imag} 0.1392405
## [641] {imag,method,network,neural} => {convolut} 0.1392405
## [642] {imag,network,neural,convolut} => {method} 0.1392405
## [643] {method,approach,neural,convolut} => {network} 0.1012658
## [644] {method,network,approach,convolut} => {neural} 0.1012658
## [645] {method,network,approach,neural} => {convolut} 0.1012658
## [646] {method,data,neural,convolut} => {network} 0.1097046
## [647] {method,data,network,convolut} => {neural} 0.1097046
## [648] {method,data,network,neural} => {convolut} 0.1097046
## [649] {imag,method,data,neural} => {network} 0.1012658
## [650] {imag,method,data,network} => {neural} 0.1012658
## [651] {method,data,network,neural} => {imag} 0.1012658
## [652] {imag,method,train,neural} => {network} 0.1012658
## [653] {imag,method,train,network} => {neural} 0.1012658
## [654] {method,train,network,neural} => {imag} 0.1012658
## [655] {imag,train,network,neural} => {method} 0.1012658
## [656] {imag,approach,neural,convolut} => {network} 0.1139241
## [657] {imag,network,approach,convolut} => {neural} 0.1139241
## [658] {imag,network,approach,neural} => {convolut} 0.1139241
## [659] {imag,data,neural,convolut} => {network} 0.1097046
## [660] {imag,data,network,convolut} => {neural} 0.1097046
## [661] {imag,data,network,neural} => {convolut} 0.1097046
## [662] {propos,approach,neural,convolut} => {network} 0.1012658
## [663] {propos,network,approach,convolut} => {neural} 0.1012658
## [664] {propos,network,approach,neural} => {convolut} 0.1012658
## [665] {data,approach,neural,convolut} => {network} 0.1097046
## [666] {data,network,approach,convolut} => {neural} 0.1097046
## [667] {data,network,approach,neural} => {convolut} 0.1097046
## confidence lift count
## [1] 0.8484848 1.1759702 28
## [2] 0.8333333 1.1549708 25
## [3] 0.8064516 1.1177136 25
## [4] 0.7575758 1.0499734 25
## [5] 0.8437500 1.8515625 27
## [6] 0.7500000 1.0394737 24
## [7] 0.7741935 1.0730051 24
## [8] 0.8285714 1.1483709 29
## [9] 0.7352941 1.6135621 25
## [10] 0.8333333 1.1549708 30
## [11] 0.7142857 0.9899749 25
## [12] 0.9142857 2.3051672 32
## [13] 0.8857143 1.4993878 31
## [14] 0.9428571 1.3067669 33
## [15] 0.7647059 1.0598555 26
## [16] 0.8055556 1.1164717 29
## [17] 0.7777778 1.0779727 28
## [18] 0.7631579 1.0577101 29
## [19] 0.7750000 1.0741228 31
## [20] 0.7179487 0.9950517 28
## [21] 0.7073171 1.1973868 29
## [22] 0.8780488 1.2169448 36
## [23] 0.7674419 1.0636475 33
## [24] 0.7250000 1.0048246 29
## [25] 0.7674419 1.0636475 33
## [26] 0.7173913 1.2144410 33
## [27] 0.7608696 1.0545385 35
## [28] 0.7111111 0.9855750 32
## [29] 0.7441860 1.0314157 32
## [30] 0.7346939 1.0182599 36
## [31] 0.7500000 1.0394737 36
## [32] 0.7555556 1.0471735 34
## [33] 0.7346939 1.0182599 36
## [34] 0.7608696 1.0545385 35
## [35] 0.7446809 1.0321015 35
## [36] 0.7333333 1.0163743 33
## [37] 0.7115385 0.9861673 37
## [38] 0.7547170 1.0460113 40
## [39] 0.8571429 1.1879699 42
## [40] 0.7254902 1.4571286 37
## [41] 0.7058824 1.1949580 36
## [42] 0.7450980 1.0326797 38
## [43] 0.7254902 1.0055040 37
## [44] 0.7222222 1.0009747 39
## [45] 0.8305085 1.1510556 49
## [46] 0.7169811 0.9937107 38
## [47] 0.7321429 1.0147243 41
## [48] 0.8281250 1.1477522 53
## [49] 0.7758621 1.0753176 45
## [50] 0.7666667 1.0625731 46
## [51] 0.7692308 1.0661269 50
## [52] 0.7384615 1.0234818 48
## [53] 0.7424242 1.0289740 49
## [54] 0.7222222 1.0009747 52
## [55] 0.7564103 1.0483581 59
## [56] 0.7000000 0.9701754 56
## [57] 0.7444444 1.0317739 67
## [58] 0.7938144 1.1001990 77
## [59] 0.7422680 1.0287575 72
## [60] 0.8723404 1.4767477 82
## [61] 0.9574468 1.3269877 90
## [62] 0.8125000 1.1260965 78
## [63] 0.7708333 1.0683480 74
## [64] 0.7254902 1.0055040 74
## [65] 0.7596154 1.0528003 79
## [66] 0.7777778 1.0779727 84
## [67] 0.7314815 1.0138077 79
## [68] 0.7678571 1.0642231 86
## [69] 0.9714286 1.3463659 136
## [70] 0.7953216 1.3463659 136
## [71] 0.9687500 1.6399554 31
## [72] 1.0000000 2.5212766 31
## [73] 1.0000000 1.3859649 32
## [74] 0.9696970 2.4448743 32
## [75] 1.0000000 1.3859649 31
## [76] 0.9393939 1.5902597 31
## [77] 0.9615385 1.3326586 25
## [78] 0.9259259 1.5674603 25
## [79] 0.9259259 1.2833008 25
## [80] 0.8064516 1.3652074 25
## [81] 1.0000000 1.3859649 29
## [82] 0.8055556 1.3636905 29
## [83] 0.9600000 1.3305263 24
## [84] 0.7272727 1.2311688 24
## [85] 1.0000000 1.3859649 26
## [86] 0.8965517 1.5177340 26
## [87] 1.0000000 1.3859649 29
## [88] 0.8787879 1.4876623 29
## [89] 1.0000000 1.3859649 24
## [90] 0.7741935 1.3105991 24
## [91] 0.9259259 1.2833008 25
## [92] 0.9259259 1.5674603 25
## [93] 0.8000000 1.3542857 24
## [94] 0.7272727 1.7954545 24
## [95] 0.8000000 1.1087719 24
## [96] 0.9696970 1.3439660 32
## [97] 0.9142857 1.5477551 32
## [98] 1.0000000 1.3859649 27
## [99] 0.8437500 1.4283482 27
## [100] 1.0000000 1.3859649 27
## [101] 0.8437500 1.4283482 27
## [102] 1.0000000 1.3859649 32
## [103] 0.8888889 1.5047619 32
## [104] 0.9642857 1.3364662 27
## [105] 0.7500000 1.2696429 27
## [106] 1.0000000 1.3859649 29
## [107] 0.8529412 1.4439076 29
## [108] 0.7878788 1.0919724 26
## [109] 0.7222222 1.6781046 26
## [110] 0.9666667 1.3397661 29
## [111] 0.8055556 1.3636905 29
## [112] 1.0000000 1.3859649 28
## [113] 0.8000000 1.3542857 28
## [114] 0.9666667 1.3397661 29
## [115] 0.8285714 1.4026531 29
## [116] 1.0000000 1.3859649 27
## [117] 0.9000000 1.5235714 27
## [118] 1.0000000 1.3859649 29
## [119] 0.8787879 1.4876623 29
## [120] 1.0000000 1.3859649 27
## [121] 0.7297297 1.2353282 27
## [122] 0.9696970 1.3439660 32
## [123] 0.8000000 1.3542857 32
## [124] 0.9310345 1.2903811 27
## [125] 1.0000000 1.3859649 31
## [126] 0.7380952 1.2494898 31
## [127] 1.0000000 1.3859649 30
## [128] 0.9090909 1.5389610 30
## [129] 0.9722222 1.3474659 35
## [130] 0.9210526 1.5592105 35
## [131] 0.7812500 1.0827851 25
## [132] 0.9354839 1.2965478 29
## [133] 0.7837838 1.3268340 29
## [134] 0.8064516 1.1177136 25
## [135] 0.9677419 1.3412564 30
## [136] 0.7692308 1.3021978 30
## [137] 0.8666667 1.2011696 26
## [138] 0.9629630 1.6301587 26
## [139] 1.0000000 1.3859649 27
## [140] 0.8620690 1.1947973 25
## [141] 0.8888889 1.2319688 24
## [142] 0.7812500 1.0827851 25
## [143] 0.8275862 1.4009852 24
## [144] 0.9310345 1.2903811 27
## [145] 1.0000000 1.3859649 41
## [146] 0.8367347 1.4164723 41
## [147] 1.0000000 1.3859649 24
## [148] 1.0000000 1.3859649 32
## [149] 0.8421053 1.4255639 32
## [150] 0.9714286 1.3463659 34
## [151] 0.8292683 1.4038328 34
## [152] 1.0000000 1.3859649 26
## [153] 0.7428571 1.0295739 26
## [154] 1.0000000 1.3859649 35
## [155] 0.8536585 1.4451220 35
## [156] 0.8666667 1.2011696 26
## [157] 0.8064516 1.3652074 25
## [158] 0.9677419 1.3412564 30
## [159] 0.8571429 1.1879699 24
## [160] 0.9000000 1.2473684 27
## [161] 0.8888889 1.2319688 24
## [162] 0.9259259 1.2833008 25
## [163] 0.8928571 1.2374687 25
## [164] 1.0000000 1.3859649 38
## [165] 0.7169811 1.2137466 38
## [166] 0.9615385 1.3326586 25
## [167] 0.8064516 1.1177136 25
## [168] 0.8064516 1.1177136 25
## [169] 0.8181818 1.1339713 27
## [170] 0.8333333 1.1549708 30
## [171] 0.7500000 1.0394737 24
## [172] 0.9729730 1.3485064 36
## [173] 0.8000000 1.3542857 36
## [174] 0.7812500 1.0827851 25
## [175] 0.7058824 0.9783282 24
## [176] 1.0000000 1.3859649 37
## [177] 0.8043478 1.3616460 37
## [178] 0.9696970 1.3439660 32
## [179] 0.7804878 1.3212544 32
## [180] 0.7027027 1.1895753 26
## [181] 0.8108108 1.1237553 30
## [182] 0.8888889 1.5047619 24
## [183] 0.9259259 1.2833008 25
## [184] 0.7352941 1.6756222 25
## [185] 0.7058824 1.1949580 24
## [186] 0.8235294 1.1413829 28
## [187] 0.7567568 1.0488383 28
## [188] 0.8611111 1.1934698 31
## [189] 0.9761905 1.3529657 41
## [190] 0.8200000 1.3881429 41
## [191] 0.7500000 1.0394737 27
## [192] 0.9411765 1.3044376 32
## [193] 0.7272727 1.2311688 32
## [194] 0.8064516 1.1177136 25
## [195] 0.8000000 1.1087719 24
## [196] 0.7631579 1.2919173 29
## [197] 0.7073171 1.4967334 29
## [198] 0.7631579 1.0577101 29
## [199] 0.9268293 1.2845528 38
## [200] 0.7916667 1.3401786 38
## [201] 1.0000000 1.6928571 27
## [202] 0.9629630 1.3346329 26
## [203] 0.7500000 1.2696429 24
## [204] 0.7812500 1.0827851 25
## [205] 0.7500000 1.2696429 24
## [206] 0.8750000 1.2127193 28
## [207] 0.7575758 1.0499734 25
## [208] 0.7272727 1.0079745 24
## [209] 0.7027027 1.1895753 26
## [210] 0.7837838 1.0862968 29
## [211] 0.9777778 1.3551657 44
## [212] 0.8979592 1.5201166 44
## [213] 0.8387097 1.4198157 26
## [214] 0.9677419 1.3412564 30
## [215] 0.7894737 1.0941828 30
## [216] 0.7750000 1.0741228 31
## [217] 0.7027027 0.9739213 26
## [218] 0.7714286 1.0691729 27
## [219] 0.8333333 1.1549708 30
## [220] 0.7500000 1.0394737 24
## [221] 0.7105263 0.9847645 27
## [222] 0.9767442 1.3537332 42
## [223] 0.8076923 1.3673077 42
## [224] 0.7567568 1.6606607 28
## [225] 0.7027027 1.1895753 26
## [226] 0.7567568 1.0488383 28
## [227] 0.7804878 1.0817287 32
## [228] 0.7222222 1.2226190 26
## [229] 0.8055556 1.1164717 29
## [230] 0.8918919 1.5098456 33
## [231] 0.9729730 1.3485064 36
## [232] 0.8000000 1.3542857 24
## [233] 0.9000000 1.2473684 27
## [234] 0.7894737 1.0941828 30
## [235] 0.7714286 1.0691729 27
## [236] 0.7659574 1.0615901 36
## [237] 0.7619048 1.0559733 32
## [238] 0.8108108 1.1237553 30
## [239] 0.7272727 1.2311688 24
## [240] 0.7575758 1.0499734 25
## [241] 0.9803922 1.3587891 50
## [242] 0.8474576 1.4346247 50
## [243] 0.7575758 1.0499734 25
## [244] 0.9393939 1.5902597 31
## [245] 0.7045455 1.7763540 31
## [246] 0.9696970 1.3439660 32
## [247] 0.7812500 1.3225446 25
## [248] 0.8125000 1.1260965 26
## [249] 0.7500000 1.0394737 30
## [250] 0.7222222 1.0009747 26
## [251] 0.8285714 1.1483709 29
## [252] 0.7368421 1.0212373 28
## [253] 0.7837838 1.0862968 29
## [254] 0.9545455 1.3229665 42
## [255] 0.7500000 1.2696429 42
## [256] 0.7777778 1.0779727 28
## [257] 0.7631579 1.0577101 29
## [258] 0.7073171 1.5521680 29
## [259] 0.8780488 1.4864111 36
## [260] 0.9512195 1.3183569 39
## [261] 0.7619048 1.0559733 32
## [262] 0.7619048 1.0559733 32
## [263] 0.7105263 1.2028195 27
## [264] 0.8421053 1.1671283 32
## [265] 0.7173913 0.9942792 33
## [266] 0.7678571 1.0642231 43
## [267] 0.7959184 1.1031149 39
## [268] 0.7560976 1.0479247 31
## [269] 0.9655172 1.3381730 56
## [270] 0.8358209 1.4149254 56
## [271] 0.8709677 1.4744240 27
## [272] 0.9354839 1.2965478 29
## [273] 0.7619048 1.0559733 32
## [274] 0.7142857 0.9899749 30
## [275] 0.7111111 0.9855750 32
## [276] 0.9574468 1.3269877 45
## [277] 0.8035714 1.3603316 45
## [278] 0.8043478 1.1147979 37
## [279] 0.9000000 1.5235714 36
## [280] 0.9750000 1.3513158 39
## [281] 0.7500000 1.2696429 30
## [282] 0.9000000 1.2473684 36
## [283] 0.8604651 1.1925745 37
## [284] 0.7750000 1.0741228 31
## [285] 0.8181818 1.1339713 45
## [286] 0.8260870 1.1449275 38
## [287] 0.7111111 1.2038095 32
## [288] 0.8888889 1.2319688 40
## [289] 0.7500000 1.2696429 33
## [290] 0.8863636 1.2284689 39
## [291] 0.7111111 1.2038095 32
## [292] 0.7111111 0.9855750 32
## [293] 0.9687500 1.3426535 62
## [294] 0.8051948 1.3630798 62
## [295] 0.7346939 1.8137755 36
## [296] 0.8775510 1.4855685 43
## [297] 0.7049180 1.7772933 43
## [298] 0.9795918 1.3576799 48
## [299] 0.7735849 1.3095687 41
## [300] 0.8679245 1.2029129 46
## [301] 0.7317073 1.2386760 30
## [302] 0.8048780 1.1155327 33
## [303] 0.7179487 0.9950517 28
## [304] 0.7291667 1.0105994 35
## [305] 0.7551020 1.0465449 37
## [306] 0.7021277 1.1886018 33
## [307] 0.7872340 1.0910788 37
## [308] 0.8000000 1.1087719 40
## [309] 0.9836066 1.3632442 60
## [310] 0.8333333 1.4107143 60
## [311] 0.8909091 1.5081818 49
## [312] 0.7313433 1.8439187 49
## [313] 0.9636364 1.3355662 53
## [314] 0.9200000 1.5574286 46
## [315] 0.7540984 1.9012905 46
## [316] 0.9800000 1.3582456 49
## [317] 0.8571429 1.4510204 36
## [318] 1.0000000 1.3859649 42
## [319] 0.8604651 1.4566445 37
## [320] 0.9767442 1.3537332 42
## [321] 0.8333333 1.4107143 40
## [322] 0.9583333 1.3282164 46
## [323] 0.9069767 1.5353821 39
## [324] 0.9767442 1.3537332 42
## [325] 0.8444444 1.4295238 38
## [326] 0.9111111 1.2627680 41
## [327] 0.9512195 1.6102787 39
## [328] 0.9268293 1.2845528 38
## [329] 0.9756098 1.3521609 80
## [330] 0.8888889 1.5047619 80
## [331] 0.8181818 1.1339713 36
## [332] 0.8222222 1.1395712 37
## [333] 0.8085106 1.1205674 38
## [334] 0.8409091 1.1654705 37
## [335] 0.7659574 1.2966565 36
## [336] 0.8297872 1.1500560 39
## [337] 0.7500000 1.2696429 36
## [338] 0.8333333 1.1549708 40
## [339] 0.9701493 1.3445928 65
## [340] 0.8333333 1.4107143 65
## [341] 0.7142857 1.6277473 30
## [342] 0.7857143 1.0889724 33
## [343] 0.7818182 1.0835726 43
## [344] 0.8260870 1.1449275 38
## [345] 0.7924528 1.0983118 42
## [346] 0.8292683 1.1493368 34
## [347] 0.7058824 0.9783282 36
## [348] 0.9672131 1.3405234 59
## [349] 0.7972973 1.3497104 59
## [350] 0.7692308 1.0661269 40
## [351] 0.7804878 1.0817287 32
## [352] 0.7045455 1.1926948 31
## [353] 0.8409091 1.1654705 37
## [354] 0.7959184 1.1031149 39
## [355] 0.9830508 1.3624740 58
## [356] 0.7837838 1.3268340 58
## [357] 0.7800000 1.0810526 39
## [358] 0.8297872 1.1500560 39
## [359] 0.7800000 1.0810526 39
## [360] 0.9672131 1.3405234 59
## [361] 0.7468354 1.2642857 59
## [362] 0.8000000 1.1087719 40
## [363] 0.7916667 1.0972222 38
## [364] 0.7000000 0.9701754 35
## [365] 0.9701493 1.3445928 65
## [366] 0.7738095 1.3099490 65
## [367] 0.7719298 1.0698677 44
## [368] 0.9848485 1.3649654 65
## [369] 0.8227848 1.3928571 65
## [370] 0.9571429 1.3265664 67
## [371] 0.7790698 1.3188538 67
## [372] 0.9571429 1.3265664 67
## [373] 0.8701299 1.4730056 67
## [374] 1.0000000 1.3859649 31
## [375] 0.9687500 1.6399554 31
## [376] 1.0000000 2.5212766 31
## [377] 1.0000000 1.3859649 26
## [378] 0.9629630 1.6301587 26
## [379] 1.0000000 1.3859649 24
## [380] 0.8888889 1.5047619 24
## [381] 1.0000000 1.3859649 25
## [382] 0.8333333 1.4107143 25
## [383] 0.9615385 1.3326586 25
## [384] 0.8333333 1.4107143 25
## [385] 1.0000000 1.3859649 24
## [386] 0.8571429 1.4510204 24
## [387] 0.8965517 1.2425892 26
## [388] 0.8965517 1.5177340 26
## [389] 0.9629630 1.3346329 26
## [390] 1.0000000 1.6928571 26
## [391] 1.0000000 1.3859649 24
## [392] 0.8571429 1.4510204 24
## [393] 0.9615385 1.3326586 25
## [394] 0.8620690 1.4593596 25
## [395] 0.9615385 1.3326586 25
## [396] 0.8333333 1.4107143 25
## [397] 0.9615385 1.3326586 25
## [398] 0.8064516 1.3652074 25
## [399] 1.0000000 1.3859649 26
## [400] 0.9285714 1.5719388 26
## [401] 0.9629630 1.3346329 26
## [402] 0.8125000 1.3754464 26
## [403] 1.0000000 1.3859649 26
## [404] 0.8965517 1.5177340 26
## [405] 1.0000000 1.3859649 33
## [406] 0.9166667 1.5517857 33
## [407] 1.0000000 1.3859649 24
## [408] 0.8888889 1.5047619 24
## [409] 1.0000000 1.3859649 26
## [410] 0.8666667 1.4671429 26
## [411] 1.0000000 1.3859649 27
## [412] 0.7500000 1.2696429 27
## [413] 1.0000000 1.3859649 27
## [414] 0.8437500 1.4283482 27
## [415] 1.0000000 1.3859649 24
## [416] 0.8000000 1.3542857 24
## [417] 1.0000000 1.3859649 24
## [418] 0.9600000 1.6251429 24
## [419] 0.9677419 1.3412564 30
## [420] 0.9375000 1.5870536 30
## [421] 0.7142857 1.8009119 30
## [422] 0.9600000 1.3305263 24
## [423] 0.9230769 1.5626374 24
## [424] 1.0000000 1.3859649 24
## [425] 0.8571429 1.4510204 24
## [426] 0.9615385 1.3326586 25
## [427] 0.8620690 1.4593596 25
## [428] 0.9600000 1.6251429 24
## [429] 0.8275862 2.0865737 24
## [430] 0.9600000 1.3305263 24
## [431] 0.7500000 1.8909574 24
## [432] 0.8620690 1.4593596 25
## [433] 0.9310345 1.2903811 27
## [434] 0.9722222 1.3474659 35
## [435] 0.8974359 1.5192308 35
## [436] 0.9655172 1.3381730 28
## [437] 0.8750000 1.4812500 28
## [438] 0.9285714 1.2869674 26
## [439] 0.8125000 1.3754464 26
## [440] 1.0000000 1.3859649 27
## [441] 0.8437500 1.4283482 27
## [442] 0.9310345 1.2903811 27
## [443] 0.8181818 1.3850649 27
## [444] 0.8275862 1.1470054 24
## [445] 0.9473684 1.3130194 36
## [446] 0.8372093 1.4172757 36
## [447] 0.9696970 1.3439660 32
## [448] 0.8205128 1.3890110 32
## [449] 0.9230769 1.2793522 24
## [450] 0.7741935 1.3105991 24
## [451] 0.9600000 1.3305263 24
## [452] 0.8275862 1.4009852 24
## [453] 0.9629630 1.3346329 26
## [454] 0.8965517 1.5177340 26
## [455] 0.9629630 1.3346329 26
## [456] 0.8125000 1.3754464 26
## [457] 0.9230769 1.2793522 24
## [458] 0.7500000 1.2696429 24
## [459] 0.9615385 1.3326586 25
## [460] 0.8620690 1.4593596 25
## [461] 0.9333333 1.2935673 28
## [462] 0.8750000 1.4812500 28
## [463] 0.9600000 1.3305263 24
## [464] 0.8571429 1.4510204 24
## [465] 1.0000000 1.3859649 29
## [466] 0.7837838 1.3268340 29
## [467] 1.0000000 1.3859649 25
## [468] 1.0000000 1.3859649 25
## [469] 0.9722222 1.3474659 35
## [470] 0.8974359 1.5192308 35
## [471] 0.9615385 1.3326586 25
## [472] 0.9666667 1.3397661 29
## [473] 0.8055556 1.3636905 29
## [474] 0.8275862 1.1470054 24
## [475] 1.0000000 1.3859649 28
## [476] 0.7567568 1.2810811 28
## [477] 0.9615385 1.3326586 25
## [478] 0.8064516 1.3652074 25
## [479] 0.8333333 1.1549708 25
## [480] 1.0000000 1.3859649 33
## [481] 0.7333333 1.2414286 33
## [482] 1.0000000 1.3859649 29
## [483] 0.7631579 1.2919173 29
## [484] 1.0000000 1.3859649 32
## [485] 0.8000000 1.3542857 32
## [486] 0.9696970 1.3439660 32
## [487] 0.8205128 1.3890110 32
## [488] 0.9375000 1.2993421 30
## [489] 0.9375000 1.5870536 30
## [490] 0.9166667 1.5517857 33
## [491] 0.7674419 1.8946221 33
## [492] 0.8048780 2.0293202 33
## [493] 1.0000000 1.3859649 36
## [494] 0.7500000 1.8515625 36
## [495] 0.7826087 1.9731730 36
## [496] 0.9230769 1.5626374 24
## [497] 0.8000000 2.0170213 24
## [498] 1.0000000 1.3859649 26
## [499] 0.7878788 1.9864603 26
## [500] 1.0000000 1.3859649 24
## [501] 0.9629630 1.3346329 26
## [502] 0.7027027 1.7717079 26
## [503] 0.8965517 1.5177340 26
## [504] 0.7878788 1.9864603 26
## [505] 0.9655172 1.3381730 28
## [506] 0.7567568 1.9079931 28
## [507] 1.0000000 1.3859649 43
## [508] 0.8958333 1.5165179 43
## [509] 0.7166667 1.8069149 43
## [510] 0.8888889 1.2319688 24
## [511] 0.8275862 1.4009852 24
## [512] 0.7272727 1.7954545 24
## [513] 0.8965517 1.2425892 26
## [514] 0.7027027 1.7347973 26
## [515] 0.7741935 1.3105991 24
## [516] 0.7272727 1.7954545 24
## [517] 0.8709677 1.2071307 27
## [518] 1.0000000 1.3859649 41
## [519] 0.8913043 1.5088509 41
## [520] 0.9666667 1.3397661 29
## [521] 0.8787879 1.4876623 29
## [522] 0.9655172 1.3381730 28
## [523] 0.8000000 1.3542857 28
## [524] 0.9655172 1.3381730 28
## [525] 0.7567568 1.2810811 28
## [526] 0.9696970 1.3439660 32
## [527] 0.8648649 1.4640927 32
## [528] 0.9696970 1.3439660 32
## [529] 0.8000000 1.3542857 32
## [530] 0.9642857 1.3364662 27
## [531] 0.9310345 1.5761084 27
## [532] 0.9333333 1.5800000 28
## [533] 0.9333333 2.3531915 28
## [534] 0.9666667 1.3397661 29
## [535] 0.8055556 2.0310284 29
## [536] 1.0000000 1.3859649 27
## [537] 0.7297297 1.8398505 27
## [538] 0.9615385 1.3326586 25
## [539] 0.9642857 1.3364662 27
## [540] 0.7297297 1.8398505 27
## [541] 0.9629630 1.6301587 26
## [542] 0.7222222 1.8209220 26
## [543] 1.0000000 1.3859649 27
## [544] 0.9259259 1.5674603 25
## [545] 0.9259259 1.2833008 25
## [546] 0.9591837 1.3293949 47
## [547] 0.8867925 1.5012129 47
## [548] 0.7230769 1.8230769 47
## [549] 1.0000000 1.3859649 24
## [550] 0.7272727 1.8336557 24
## [551] 0.8928571 1.5114796 25
## [552] 0.7352941 1.8538798 25
## [553] 0.9642857 1.3364662 27
## [554] 0.9615385 1.3326586 25
## [555] 0.8965517 1.5177340 26
## [556] 0.7647059 1.9280350 26
## [557] 1.0000000 1.3859649 29
## [558] 0.9782609 1.3558352 45
## [559] 0.9183673 1.5546647 45
## [560] 0.7627119 1.9230076 45
## [561] 1.0000000 1.3859649 24
## [562] 1.0000000 1.3859649 36
## [563] 0.8571429 1.4510204 36
## [564] 0.9615385 1.3326586 25
## [565] 0.9729730 1.3485064 36
## [566] 0.8571429 1.4510204 36
## [567] 0.9750000 1.3513158 39
## [568] 0.8478261 1.4352484 39
## [569] 0.9600000 1.3305263 24
## [570] 1.0000000 1.3859649 39
## [571] 0.9285714 1.5719388 39
## [572] 0.9473684 1.3130194 36
## [573] 0.8780488 1.4864111 36
## [574] 0.9743590 1.3504274 38
## [575] 1.0000000 1.6928571 38
## [576] 0.9666667 1.3397661 29
## [577] 0.8055556 1.3636905 29
## [578] 1.0000000 1.3859649 30
## [579] 0.8108108 1.3725869 30
## [580] 0.9666667 1.3397661 29
## [581] 0.7631579 1.2919173 29
## [582] 0.9642857 1.3364662 27
## [583] 0.7297297 1.2353282 27
## [584] 1.0000000 1.3859649 36
## [585] 0.9230769 1.5626374 36
## [586] 0.9444444 1.3089669 34
## [587] 0.8500000 1.4389286 34
## [588] 0.9615385 1.3326586 25
## [589] 0.9259259 1.5674603 25
## [590] 0.8333333 1.1549708 25
## [591] 0.7575758 1.7263986 25
## [592] 1.0000000 1.3859649 26
## [593] 0.7878788 1.3337662 26
## [594] 0.8064516 1.1177136 25
## [595] 0.8125000 1.1260965 26
## [596] 0.9411765 1.3044376 32
## [597] 0.7441860 1.2598007 32
## [598] 0.9354839 1.2965478 29
## [599] 0.7631579 1.2919173 29
## [600] 0.9705882 1.3452012 33
## [601] 0.7857143 1.3301020 33
## [602] 0.9285714 1.2869674 26
## [603] 0.7647059 1.2945378 26
## [604] 0.9696970 1.3439660 32
## [605] 0.8888889 1.5047619 32
## [606] 1.0000000 1.3859649 29
## [607] 0.7250000 1.2273214 29
## [608] 1.0000000 1.3859649 26
## [609] 0.8125000 1.3754464 26
## [610] 1.0000000 1.3859649 31
## [611] 0.8378378 1.4183398 31
## [612] 1.0000000 1.3859649 30
## [613] 0.7692308 1.3021978 30
## [614] 0.9642857 1.3364662 27
## [615] 0.9310345 1.5761084 27
## [616] 0.9354839 1.2965478 29
## [617] 0.7435897 1.2587912 29
## [618] 0.9677419 1.3412564 30
## [619] 0.7692308 1.3021978 30
## [620] 0.9375000 1.2993421 30
## [621] 0.7692308 1.3021978 30
## [622] 0.9696970 1.3439660 32
## [623] 0.8648649 1.4640927 32
## [624] 0.9687500 1.3426535 31
## [625] 0.7750000 1.3119643 31
## [626] 0.9375000 1.2993421 30
## [627] 0.7894737 1.3364662 30
## [628] 0.9666667 1.3397661 29
## [629] 0.8285714 1.4026531 29
## [630] 0.9743590 1.3504274 38
## [631] 0.8636364 1.4620130 38
## [632] 0.9696970 1.3439660 32
## [633] 0.8205128 1.3890110 32
## [634] 0.9411765 1.3044376 32
## [635] 0.8648649 1.4640927 32
## [636] 0.9600000 1.3305263 24
## [637] 0.8888889 1.5047619 24
## [638] 1.0000000 1.3859649 33
## [639] 0.9166667 1.5517857 33
## [640] 0.7674419 1.8946221 33
## [641] 0.8048780 2.0293202 33
## [642] 0.7021277 1.7155078 33
## [643] 1.0000000 1.3859649 24
## [644] 0.9230769 1.5626374 24
## [645] 0.8275862 2.0865737 24
## [646] 1.0000000 1.3859649 26
## [647] 0.9285714 1.5719388 26
## [648] 0.8125000 2.0485372 26
## [649] 1.0000000 1.3859649 24
## [650] 0.9230769 1.5626374 24
## [651] 0.7500000 1.8515625 24
## [652] 1.0000000 1.3859649 24
## [653] 0.8888889 1.5047619 24
## [654] 0.7500000 1.8515625 24
## [655] 0.7058824 1.7246816 24
## [656] 0.9642857 1.3364662 27
## [657] 0.9310345 1.5761084 27
## [658] 0.9310345 2.3473955 27
## [659] 1.0000000 1.3859649 26
## [660] 0.9629630 1.6301587 26
## [661] 0.7222222 1.8209220 26
## [662] 0.9600000 1.3305263 24
## [663] 0.8888889 1.5047619 24
## [664] 0.7500000 1.8909574 24
## [665] 1.0000000 1.3859649 26
## [666] 0.8965517 1.5177340 26
## [667] 0.7878788 1.9864603 26## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 62
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[5767 item(s), 622 transaction(s)] done [0.00s].
## sorting and recoding items ... [103 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [364 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
## lhs rhs support confidence
## [1] {cnn} => {convolut} 0.1012862 0.8513514
## [2] {cnn} => {neural} 0.1045016 0.8783784
## [3] {cnn} => {network} 0.1125402 0.9459459
## [4] {outperform} => {network} 0.1012862 0.7590361
## [5] {general} => {network} 0.1045016 0.7738095
## [6] {number} => {network} 0.1028939 0.7901235
## [7] {input} => {network} 0.1093248 0.8500000
## [8] {layer} => {network} 0.1189711 0.8809524
## [9] {effici} => {network} 0.1061093 0.7415730
## [10] {test} => {network} 0.1157556 0.7578947
## [11] {complex} => {network} 0.1093248 0.7727273
## [12] {design} => {network} 0.1012862 0.7078652
## [13] {optim} => {network} 0.1189711 0.7708333
## [14] {function} => {network} 0.1221865 0.7755102
## [15] {requir} => {network} 0.1077170 0.7052632
## [16] {appli} => {network} 0.1254019 0.7500000
## [17] {includ} => {network} 0.1205788 0.7653061
## [18] {detect} => {network} 0.1205788 0.7009346
## [19] {larg} => {network} 0.1318328 0.7663551
## [20] {stateoftheart} => {network} 0.1286174 0.7476636
## [21] {analysi} => {network} 0.1318328 0.7387387
## [22] {inform} => {network} 0.1302251 0.7043478
## [23] {set} => {network} 0.1463023 0.7844828
## [24] {learn} => {network} 0.1350482 0.7000000
## [25] {evalu} => {network} 0.1463023 0.7520661
## [26] {time} => {network} 0.1559486 0.8151261
## [27] {signific} => {network} 0.1398714 0.7250000
## [28] {studi} => {network} 0.1655949 0.8240000
## [29] {compar} => {network} 0.1543408 0.7384615
## [30] {process} => {network} 0.1511254 0.7343750
## [31] {architectur} => {network} 0.1704180 0.7737226
## [32] {work} => {network} 0.1623794 0.7112676
## [33] {problem} => {network} 0.1575563 0.7000000
## [34] {classif} => {network} 0.1736334 0.7500000
## [35] {predict} => {network} 0.1768489 0.7482993
## [36] {high} => {network} 0.1655949 0.7006803
## [37] {algorithm} => {network} 0.1816720 0.7434211
## [38] {accuraci} => {network} 0.1864952 0.7532468
## [39] {achiev} => {network} 0.1881029 0.7597403
## [40] {dataset} => {network} 0.1945338 0.7469136
## [41] {demonstr} => {network} 0.1816720 0.7243590
## [42] {task} => {network} 0.1929260 0.7407407
## [43] {improv} => {network} 0.1848875 0.7324841
## [44] {featur} => {network} 0.1961415 0.7261905
## [45] {comput} => {network} 0.2041801 0.7134831
## [46] {convolut} => {neural} 0.2765273 0.8686869
## [47] {convolut} => {network} 0.3038585 0.9545455
## [48] {imag} => {network} 0.2491961 0.7345972
## [49] {base} => {network} 0.2524116 0.7370892
## [50] {show} => {network} 0.2733119 0.7623318
## [51] {paper} => {network} 0.3054662 0.7089552
## [52] {perform} => {network} 0.3231511 0.7416974
## [53] {propos} => {network} 0.3376206 0.7664234
## [54] {method} => {network} 0.3135048 0.7116788
## [55] {result} => {network} 0.3135048 0.7142857
## [56] {data} => {network} 0.3295820 0.7243816
## [57] {train} => {network} 0.3665595 0.7755102
## [58] {model} => {network} 0.3569132 0.7138264
## [59] {neural} => {network} 0.5530547 0.9800570
## [60] {network} => {neural} 0.5530547 0.7818182
## [61] {neural,cnn} => {network} 0.1012862 0.9692308
## [62] {network,cnn} => {neural} 0.1012862 0.9000000
## [63] {appli,neural} => {network} 0.1012862 0.9843750
## [64] {appli,network} => {neural} 0.1012862 0.8076923
## [65] {neural,larg} => {network} 0.1012862 1.0000000
## [66] {network,larg} => {neural} 0.1012862 0.7682927
## [67] {neural,effect} => {network} 0.1012862 0.9843750
## [68] {network,effect} => {neural} 0.1012862 0.8076923
## [69] {neural,inform} => {network} 0.1028939 0.9552239
## [70] {network,inform} => {neural} 0.1028939 0.7901235
## [71] {neural,set} => {network} 0.1077170 0.9710145
## [72] {network,set} => {neural} 0.1077170 0.7362637
## [73] {neural,learn} => {network} 0.1012862 0.9843750
## [74] {network,learn} => {neural} 0.1012862 0.7500000
## [75] {evalu,neural} => {network} 0.1109325 1.0000000
## [76] {evalu,network} => {neural} 0.1109325 0.7582418
## [77] {neural,time} => {network} 0.1141479 0.9726027
## [78] {network,time} => {neural} 0.1141479 0.7319588
## [79] {neural,signific} => {network} 0.1141479 0.9726027
## [80] {network,signific} => {neural} 0.1141479 0.8160920
## [81] {neural,studi} => {network} 0.1254019 0.9873418
## [82] {network,studi} => {neural} 0.1254019 0.7572816
## [83] {develop,neural} => {network} 0.1125402 0.9859155
## [84] {develop,network} => {neural} 0.1125402 0.8139535
## [85] {neural,challeng} => {network} 0.1028939 0.9846154
## [86] {network,challeng} => {neural} 0.1028939 0.7441860
## [87] {neural,techniqu} => {network} 0.1061093 0.9850746
## [88] {network,techniqu} => {neural} 0.1061093 0.7674419
## [89] {experi,neural} => {network} 0.1077170 0.9852941
## [90] {experi,network} => {neural} 0.1077170 0.7613636
## [91] {neural,recent} => {network} 0.1012862 0.9692308
## [92] {network,recent} => {neural} 0.1012862 0.7159091
## [93] {neural,compar} => {network} 0.1157556 0.9729730
## [94] {network,compar} => {neural} 0.1157556 0.7500000
## [95] {neural,process} => {network} 0.1189711 0.9736842
## [96] {network,process} => {neural} 0.1189711 0.7872340
## [97] {neural,architectur} => {network} 0.1221865 0.9620253
## [98] {network,architectur} => {neural} 0.1221865 0.7169811
## [99] {neural,work} => {network} 0.1302251 0.9759036
## [100] {network,work} => {neural} 0.1302251 0.8019802
## [101] {machin,neural} => {network} 0.1189711 1.0000000
## [102] {machin,network} => {neural} 0.1189711 0.8131868
## [103] {neural,problem} => {network} 0.1173633 0.9733333
## [104] {network,problem} => {neural} 0.1173633 0.7448980
## [105] {neural,classif} => {network} 0.1398714 0.9560440
## [106] {network,classif} => {neural} 0.1398714 0.8055556
## [107] {applic,neural} => {network} 0.1189711 1.0000000
## [108] {applic,network} => {neural} 0.1189711 0.7872340
## [109] {model,predict} => {network} 0.1109325 0.7340426
## [110] {neural,predict} => {network} 0.1366559 1.0000000
## [111] {network,predict} => {neural} 0.1366559 0.7727273
## [112] {high,neural} => {network} 0.1302251 0.9878049
## [113] {high,network} => {neural} 0.1302251 0.7864078
## [114] {neural,algorithm} => {network} 0.1366559 0.9550562
## [115] {network,algorithm} => {neural} 0.1366559 0.7522124
## [116] {accuraci,train} => {network} 0.1061093 0.7674419
## [117] {model,accuraci} => {network} 0.1061093 0.7951807
## [118] {neural,accuraci} => {network} 0.1430868 0.9888889
## [119] {network,accuraci} => {neural} 0.1430868 0.7672414
## [120] {achiev,perform} => {network} 0.1141479 0.7717391
## [121] {propos,achiev} => {network} 0.1012862 0.7777778
## [122] {achiev,model} => {network} 0.1012862 0.7500000
## [123] {achiev,neural} => {network} 0.1446945 0.9677419
## [124] {achiev,network} => {neural} 0.1446945 0.7692308
## [125] {dataset,convolut} => {neural} 0.1012862 0.8873239
## [126] {dataset,convolut} => {network} 0.1093248 0.9577465
## [127] {dataset,paper} => {network} 0.1125402 0.8235294
## [128] {dataset,propos} => {network} 0.1077170 0.8072289
## [129] {dataset,method} => {network} 0.1012862 0.7974684
## [130] {dataset,train} => {network} 0.1109325 0.7752809
## [131] {dataset,model} => {network} 0.1141479 0.7717391
## [132] {dataset,neural} => {network} 0.1511254 0.9791667
## [133] {dataset,network} => {neural} 0.1511254 0.7768595
## [134] {demonstr,neural} => {network} 0.1382637 0.9772727
## [135] {demonstr,network} => {neural} 0.1382637 0.7610619
## [136] {paper,task} => {network} 0.1028939 0.7356322
## [137] {model,task} => {network} 0.1061093 0.7173913
## [138] {neural,task} => {network} 0.1495177 0.9893617
## [139] {network,task} => {neural} 0.1495177 0.7750000
## [140] {improv,perform} => {network} 0.1109325 0.8214286
## [141] {propos,improv} => {network} 0.1012862 0.7875000
## [142] {train,improv} => {network} 0.1093248 0.8292683
## [143] {model,improv} => {network} 0.1061093 0.7764706
## [144] {neural,improv} => {network} 0.1302251 0.9529412
## [145] {network,improv} => {neural} 0.1302251 0.7043478
## [146] {neural,system} => {network} 0.1398714 0.9666667
## [147] {network,system} => {neural} 0.1398714 0.8285714
## [148] {convolut,featur} => {network} 0.1061093 0.9565217
## [149] {featur,perform} => {network} 0.1061093 0.7333333
## [150] {method,featur} => {network} 0.1061093 0.7674419
## [151] {data,featur} => {network} 0.1061093 0.7586207
## [152] {featur,model} => {network} 0.1028939 0.7441860
## [153] {featur,neural} => {network} 0.1575563 1.0000000
## [154] {featur,network} => {neural} 0.1575563 0.8032787
## [155] {present,convolut} => {network} 0.1028939 0.9552239
## [156] {present,neural} => {network} 0.1607717 0.9803922
## [157] {present,network} => {neural} 0.1607717 0.8130081
## [158] {propos,comput} => {network} 0.1045016 0.7738095
## [159] {train,comput} => {network} 0.1045016 0.7926829
## [160] {model,comput} => {network} 0.1173633 0.7765957
## [161] {neural,comput} => {network} 0.1527331 0.9895833
## [162] {network,comput} => {neural} 0.1527331 0.7480315
## [163] {convolut,imag} => {neural} 0.1446945 0.8910891
## [164] {convolut,imag} => {network} 0.1527331 0.9405941
## [165] {base,convolut} => {neural} 0.1189711 0.8705882
## [166] {base,convolut} => {network} 0.1318328 0.9647059
## [167] {convolut,show} => {network} 0.1077170 0.9436620
## [168] {convolut,approach} => {network} 0.1077170 0.9571429
## [169] {convolut,paper} => {neural} 0.1221865 0.8837209
## [170] {convolut,paper} => {network} 0.1334405 0.9651163
## [171] {convolut,perform} => {neural} 0.1398714 0.8877551
## [172] {convolut,perform} => {network} 0.1495177 0.9489796
## [173] {propos,convolut} => {neural} 0.1237942 0.8651685
## [174] {propos,convolut} => {network} 0.1398714 0.9775281
## [175] {method,convolut} => {neural} 0.1318328 0.8913043
## [176] {method,convolut} => {network} 0.1446945 0.9782609
## [177] {result,convolut} => {neural} 0.1366559 0.8947368
## [178] {result,convolut} => {network} 0.1495177 0.9789474
## [179] {data,convolut} => {neural} 0.1318328 0.8723404
## [180] {data,convolut} => {network} 0.1446945 0.9574468
## [181] {convolut,train} => {neural} 0.1479100 0.8761905
## [182] {convolut,train} => {network} 0.1591640 0.9428571
## [183] {convolut,model} => {neural} 0.1430868 0.8476190
## [184] {convolut,model} => {network} 0.1575563 0.9333333
## [185] {convolut,neural} => {network} 0.2733119 0.9883721
## [186] {convolut,network} => {neural} 0.2733119 0.8994709
## [187] {paper,imag} => {network} 0.1077170 0.7282609
## [188] {imag,perform} => {network} 0.1254019 0.7878788
## [189] {propos,imag} => {network} 0.1318328 0.8282828
## [190] {method,imag} => {network} 0.1302251 0.7168142
## [191] {result,imag} => {network} 0.1334405 0.7410714
## [192] {data,imag} => {network} 0.1061093 0.7333333
## [193] {train,imag} => {network} 0.1430868 0.7478992
## [194] {model,imag} => {network} 0.1173633 0.7604167
## [195] {neural,imag} => {network} 0.2025723 0.9767442
## [196] {network,imag} => {neural} 0.2025723 0.8129032
## [197] {base,show} => {network} 0.1157556 0.8181818
## [198] {base,approach} => {network} 0.1205788 0.7812500
## [199] {base,paper} => {network} 0.1173633 0.7373737
## [200] {base,perform} => {network} 0.1254019 0.7722772
## [201] {propos,base} => {network} 0.1254019 0.7358491
## [202] {method,base} => {network} 0.1254019 0.7090909
## [203] {result,base} => {network} 0.1221865 0.7169811
## [204] {data,base} => {network} 0.1221865 0.7755102
## [205] {base,train} => {network} 0.1270096 0.7900000
## [206] {base,model} => {network} 0.1398714 0.7565217
## [207] {base,neural} => {network} 0.2057878 0.9846154
## [208] {base,network} => {neural} 0.2057878 0.8152866
## [209] {paper,show} => {network} 0.1286174 0.7407407
## [210] {show,perform} => {network} 0.1173633 0.7525773
## [211] {propos,show} => {network} 0.1350482 0.7706422
## [212] {method,show} => {network} 0.1286174 0.7619048
## [213] {result,show} => {network} 0.1446945 0.7563025
## [214] {data,show} => {network} 0.1334405 0.7685185
## [215] {train,show} => {network} 0.1479100 0.7796610
## [216] {model,show} => {network} 0.1382637 0.7747748
## [217] {neural,show} => {network} 0.2090032 0.9629630
## [218] {network,show} => {neural} 0.2090032 0.7647059
## [219] {approach,perform} => {network} 0.1254019 0.7155963
## [220] {propos,approach} => {network} 0.1382637 0.7543860
## [221] {approach,train} => {network} 0.1318328 0.7192982
## [222] {model,approach} => {network} 0.1237942 0.7129630
## [223] {neural,approach} => {network} 0.1913183 0.9754098
## [224] {network,approach} => {neural} 0.1913183 0.7727273
## [225] {paper,perform} => {network} 0.1414791 0.7457627
## [226] {propos,paper} => {network} 0.1832797 0.7450980
## [227] {result,paper} => {network} 0.1398714 0.7016129
## [228] {data,paper} => {network} 0.1382637 0.7166667
## [229] {paper,train} => {network} 0.1286174 0.7476636
## [230] {model,paper} => {network} 0.1543408 0.7164179
## [231] {neural,paper} => {network} 0.2250804 0.9722222
## [232] {network,paper} => {neural} 0.2250804 0.7368421
## [233] {propos,perform} => {network} 0.1575563 0.7903226
## [234] {method,perform} => {network} 0.1463023 0.7583333
## [235] {result,perform} => {network} 0.1479100 0.7731092
## [236] {data,perform} => {network} 0.1511254 0.7520000
## [237] {train,perform} => {network} 0.1704180 0.7571429
## [238] {model,perform} => {network} 0.1655949 0.7463768
## [239] {neural,perform} => {network} 0.2572347 0.9876543
## [240] {network,perform} => {neural} 0.2572347 0.7960199
## [241] {method,propos} => {network} 0.1655949 0.7103448
## [242] {propos,result} => {network} 0.1688103 0.7553957
## [243] {data,propos} => {network} 0.1655949 0.7744361
## [244] {propos,train} => {network} 0.1897106 0.8489209
## [245] {propos,model} => {network} 0.1832797 0.8028169
## [246] {propos,neural} => {network} 0.2556270 0.9814815
## [247] {propos,network} => {neural} 0.2556270 0.7571429
## [248] {data,method} => {network} 0.1639871 0.7669173
## [249] {method,train} => {network} 0.1816720 0.8188406
## [250] {method,model} => {network} 0.1672026 0.7761194
## [251] {method,neural} => {network} 0.2379421 0.9801325
## [252] {method,network} => {neural} 0.2379421 0.7589744
## [253] {result,train} => {network} 0.1720257 0.8230769
## [254] {result,model} => {network} 0.1720257 0.7642857
## [255] {result,neural} => {network} 0.2443730 0.9743590
## [256] {result,network} => {neural} 0.2443730 0.7794872
## [257] {data,train} => {network} 0.2073955 0.7771084
## [258] {data,model} => {network} 0.1864952 0.7250000
## [259] {data,neural} => {network} 0.2443730 0.9743590
## [260] {data,network} => {neural} 0.2443730 0.7414634
## [261] {model,train} => {network} 0.2073955 0.7678571
## [262] {neural,train} => {network} 0.2942122 0.9945652
## [263] {network,train} => {neural} 0.2942122 0.8026316
## [264] {model,neural} => {network} 0.2781350 0.9829545
## [265] {model,network} => {neural} 0.2781350 0.7792793
## [266] {dataset,convolut,neural} => {network} 0.1012862 1.0000000
## [267] {dataset,convolut,network} => {neural} 0.1012862 0.9264706
## [268] {convolut,neural,imag} => {network} 0.1414791 0.9777778
## [269] {convolut,network,imag} => {neural} 0.1414791 0.9263158
## [270] {base,convolut,neural} => {network} 0.1189711 1.0000000
## [271] {base,convolut,network} => {neural} 0.1189711 0.9024390
## [272] {convolut,neural,paper} => {network} 0.1205788 0.9868421
## [273] {convolut,network,paper} => {neural} 0.1205788 0.9036145
## [274] {convolut,neural,perform} => {network} 0.1382637 0.9885057
## [275] {convolut,network,perform} => {neural} 0.1382637 0.9247312
## [276] {propos,convolut,neural} => {network} 0.1237942 1.0000000
## [277] {propos,convolut,network} => {neural} 0.1237942 0.8850575
## [278] {method,convolut,neural} => {network} 0.1302251 0.9878049
## [279] {method,convolut,network} => {neural} 0.1302251 0.9000000
## [280] {result,convolut,neural} => {network} 0.1350482 0.9882353
## [281] {result,convolut,network} => {neural} 0.1350482 0.9032258
## [282] {data,convolut,neural} => {network} 0.1286174 0.9756098
## [283] {data,convolut,network} => {neural} 0.1286174 0.8888889
## [284] {convolut,neural,train} => {network} 0.1463023 0.9891304
## [285] {convolut,network,train} => {neural} 0.1463023 0.9191919
## [286] {convolut,model,neural} => {network} 0.1414791 0.9887640
## [287] {convolut,model,network} => {neural} 0.1414791 0.8979592
## [288] {neural,imag,perform} => {network} 0.1077170 0.9852941
## [289] {network,imag,perform} => {neural} 0.1077170 0.8589744
## [290] {result,neural,imag} => {network} 0.1077170 0.9852941
## [291] {result,network,imag} => {neural} 0.1077170 0.8072289
## [292] {neural,train,imag} => {network} 0.1221865 0.9870130
## [293] {network,train,imag} => {neural} 0.1221865 0.8539326
## [294] {base,neural,perform} => {network} 0.1061093 1.0000000
## [295] {base,network,perform} => {neural} 0.1061093 0.8461538
## [296] {base,neural,train} => {network} 0.1109325 1.0000000
## [297] {base,network,train} => {neural} 0.1109325 0.8734177
## [298] {base,model,neural} => {network} 0.1077170 0.9710145
## [299] {base,model,network} => {neural} 0.1077170 0.7701149
## [300] {propos,neural,show} => {network} 0.1012862 0.9692308
## [301] {propos,network,show} => {neural} 0.1012862 0.7500000
## [302] {result,neural,show} => {network} 0.1045016 0.9420290
## [303] {result,network,show} => {neural} 0.1045016 0.7222222
## [304] {neural,train,show} => {network} 0.1173633 0.9864865
## [305] {network,train,show} => {neural} 0.1173633 0.7934783
## [306] {propos,neural,approach} => {network} 0.1012862 1.0000000
## [307] {propos,network,approach} => {neural} 0.1012862 0.7325581
## [308] {neural,approach,train} => {network} 0.1028939 0.9846154
## [309] {network,approach,train} => {neural} 0.1028939 0.7804878
## [310] {neural,paper,perform} => {network} 0.1093248 1.0000000
## [311] {network,paper,perform} => {neural} 0.1093248 0.7727273
## [312] {propos,neural,paper} => {network} 0.1334405 0.9764706
## [313] {propos,network,paper} => {neural} 0.1334405 0.7280702
## [314] {neural,paper,train} => {network} 0.1012862 0.9843750
## [315] {network,paper,train} => {neural} 0.1012862 0.7875000
## [316] {model,neural,paper} => {network} 0.1125402 0.9722222
## [317] {model,network,paper} => {neural} 0.1125402 0.7291667
## [318] {propos,neural,perform} => {network} 0.1189711 1.0000000
## [319] {propos,network,perform} => {neural} 0.1189711 0.7551020
## [320] {method,neural,perform} => {network} 0.1109325 1.0000000
## [321] {method,network,perform} => {neural} 0.1109325 0.7582418
## [322] {result,neural,perform} => {network} 0.1157556 0.9729730
## [323] {result,network,perform} => {neural} 0.1157556 0.7826087
## [324] {data,neural,perform} => {network} 0.1221865 0.9870130
## [325] {data,network,perform} => {neural} 0.1221865 0.8085106
## [326] {neural,train,perform} => {network} 0.1382637 1.0000000
## [327] {network,train,perform} => {neural} 0.1382637 0.8113208
## [328] {model,neural,perform} => {network} 0.1318328 1.0000000
## [329] {model,network,perform} => {neural} 0.1318328 0.7961165
## [330] {method,propos,neural} => {network} 0.1205788 0.9740260
## [331] {method,propos,network} => {neural} 0.1205788 0.7281553
## [332] {propos,result,model} => {network} 0.1061093 0.8684211
## [333] {propos,result,neural} => {network} 0.1237942 0.9746835
## [334] {propos,result,network} => {neural} 0.1237942 0.7333333
## [335] {data,propos,train} => {network} 0.1093248 0.8395062
## [336] {data,propos,neural} => {network} 0.1189711 0.9736842
## [337] {data,propos,network} => {neural} 0.1189711 0.7184466
## [338] {propos,model,train} => {network} 0.1189711 0.8915663
## [339] {propos,neural,train} => {network} 0.1430868 1.0000000
## [340] {propos,network,train} => {neural} 0.1430868 0.7542373
## [341] {propos,model,neural} => {network} 0.1366559 0.9770115
## [342] {propos,model,network} => {neural} 0.1366559 0.7456140
## [343] {method,result,neural} => {network} 0.1028939 0.9846154
## [344] {method,result,network} => {neural} 0.1028939 0.7356322
## [345] {data,method,train} => {network} 0.1141479 0.8452381
## [346] {data,method,neural} => {network} 0.1254019 0.9750000
## [347] {data,method,network} => {neural} 0.1254019 0.7647059
## [348] {method,model,train} => {network} 0.1061093 0.8800000
## [349] {method,neural,train} => {network} 0.1366559 0.9883721
## [350] {method,network,train} => {neural} 0.1366559 0.7522124
## [351] {method,model,neural} => {network} 0.1254019 0.9750000
## [352] {method,model,network} => {neural} 0.1254019 0.7500000
## [353] {result,model,train} => {network} 0.1093248 0.8500000
## [354] {result,neural,train} => {network} 0.1430868 1.0000000
## [355] {result,network,train} => {neural} 0.1430868 0.8317757
## [356] {result,model,neural} => {network} 0.1270096 0.9753086
## [357] {result,model,network} => {neural} 0.1270096 0.7383178
## [358] {data,model,train} => {network} 0.1221865 0.7524752
## [359] {data,neural,train} => {network} 0.1623794 0.9901961
## [360] {data,network,train} => {neural} 0.1623794 0.7829457
## [361] {data,model,neural} => {network} 0.1430868 0.9780220
## [362] {data,model,network} => {neural} 0.1430868 0.7672414
## [363] {model,neural,train} => {network} 0.1639871 0.9902913
## [364] {model,network,train} => {neural} 0.1639871 0.7906977
## lift count
## [1] 2.6744472 63
## [2] 1.5565566 65
## [3] 1.3372236 70
## [4] 1.0730011 63
## [5] 1.0938853 65
## [6] 1.1169473 64
## [7] 1.2015909 68
## [8] 1.2453463 74
## [9] 1.0483146 66
## [10] 1.0713876 72
## [11] 1.0923554 68
## [12] 1.0006639 63
## [13] 1.0896780 74
## [14] 1.0962894 76
## [15] 0.9969856 67
## [16] 1.0602273 78
## [17] 1.0818646 75
## [18] 0.9908666 75
## [19] 1.0833475 82
## [20] 1.0569244 80
## [21] 1.0443079 82
## [22] 0.9956917 81
## [23] 1.1089734 91
## [24] 0.9895455 84
## [25] 1.0631480 91
## [26] 1.1522918 97
## [27] 1.0248864 87
## [28] 1.1648364 103
## [29] 1.0439161 96
## [30] 1.0381392 94
## [31] 1.0937624 106
## [32] 1.0054738 101
## [33] 0.9895455 98
## [34] 1.0602273 108
## [35] 1.0578231 110
## [36] 0.9905071 103
## [37] 1.0509270 113
## [38] 1.0648170 116
## [39] 1.0739965 117
## [40] 1.0558642 121
## [41] 1.0239802 113
## [42] 1.0471380 120
## [43] 1.0354661 115
## [44] 1.0265693 122
## [45] 1.0086057 127
## [46] 1.5393824 172
## [47] 1.3493802 189
## [48] 1.0384533 155
## [49] 1.0419761 157
## [50] 1.0776600 170
## [51] 1.0022049 190
## [52] 1.0484904 201
## [53] 1.0834439 210
## [54] 1.0060551 195
## [55] 1.0097403 195
## [56] 1.0240122 205
## [57] 1.0962894 228
## [58] 1.0090909 222
## [59] 1.3854442 344
## [60] 1.3854442 344
## [61] 1.3701399 63
## [62] 1.5948718 63
## [63] 1.3915483 63
## [64] 1.4312952 63
## [65] 1.4136364 63
## [66] 1.3614759 63
## [67] 1.3915483 63
## [68] 1.4312952 63
## [69] 1.3503392 64
## [70] 1.4001618 64
## [71] 1.3726614 67
## [72] 1.3047181 67
## [73] 1.3915483 63
## [74] 1.3290598 63
## [75] 1.4136364 69
## [76] 1.3436649 69
## [77] 1.3749066 71
## [78] 1.2970893 71
## [79] 1.3749066 71
## [80] 1.4461800 71
## [81] 1.3957422 78
## [82] 1.3419633 78
## [83] 1.3937260 70
## [84] 1.4423905 70
## [85] 1.3918881 64
## [86] 1.3187570 64
## [87] 1.3925373 66
## [88] 1.3599682 66
## [89] 1.3928476 67
## [90] 1.3491971 67
## [91] 1.3701399 63
## [92] 1.2686480 63
## [93] 1.3754300 72
## [94] 1.3290598 72
## [95] 1.3764354 74
## [96] 1.3950415 74
## [97] 1.3599540 76
## [98] 1.2705478 76
## [99] 1.3795728 81
## [100] 1.4211729 81
## [101] 1.4136364 74
## [102] 1.4410319 74
## [103] 1.3759394 73
## [104] 1.3200186 73
## [105] 1.3514985 87
## [106] 1.4275087 87
## [107] 1.4136364 74
## [108] 1.3950415 74
## [109] 1.0376692 69
## [110] 1.4136364 85
## [111] 1.3693344 85
## [112] 1.3963969 81
## [113] 1.3935773 81
## [114] 1.3501021 85
## [115] 1.3329804 85
## [116] 1.0848837 66
## [117] 1.1240964 66
## [118] 1.3979293 89
## [119] 1.3596129 89
## [120] 1.0909585 71
## [121] 1.0994949 63
## [122] 1.0602273 63
## [123] 1.3680352 90
## [124] 1.3631383 90
## [125] 1.5724088 63
## [126] 1.3539052 68
## [127] 1.1641711 70
## [128] 1.1411281 67
## [129] 1.1273303 63
## [130] 1.0959653 69
## [131] 1.0909585 71
## [132] 1.3841856 94
## [133] 1.3766570 94
## [134] 1.3815083 86
## [135] 1.3486625 86
## [136] 1.0399164 64
## [137] 1.0141304 66
## [138] 1.3985977 93
## [139] 1.3733618 93
## [140] 1.1612013 69
## [141] 1.1132386 63
## [142] 1.1722838 68
## [143] 1.0976471 66
## [144] 1.3471123 81
## [145] 1.2481605 81
## [146] 1.3665152 87
## [147] 1.4682947 87
## [148] 1.3521739 66
## [149] 1.0366667 66
## [150] 1.0848837 66
## [151] 1.0724138 66
## [152] 1.0520085 64
## [153] 1.4136364 98
## [154] 1.4234739 98
## [155] 1.3503392 64
## [156] 1.3859180 100
## [157] 1.4407153 100
## [158] 1.0938853 65
## [159] 1.1205654 65
## [160] 1.0978240 73
## [161] 1.3989110 95
## [162] 1.3255715 95
## [163] 1.5790810 90
## [164] 1.3296580 95
## [165] 1.5427518 74
## [166] 1.3637433 82
## [167] 1.3339949 67
## [168] 1.3530519 67
## [169] 1.5660240 76
## [170] 1.3643235 83
## [171] 1.5731729 87
## [172] 1.3415121 93
## [173] 1.5331477 77
## [174] 1.3818693 87
## [175] 1.5794624 82
## [176] 1.3829051 90
## [177] 1.5855451 85
## [178] 1.3838756 93
## [179] 1.5458568 82
## [180] 1.3534816 90
## [181] 1.5526794 92
## [182] 1.3328571 99
## [183] 1.5020486 89
## [184] 1.3193939 98
## [185] 1.3971987 170
## [186] 1.5939342 170
## [187] 1.0294960 67
## [188] 1.1137741 78
## [189] 1.1708907 82
## [190] 1.0133146 81
## [191] 1.0476055 83
## [192] 1.0366667 66
## [193] 1.0572574 89
## [194] 1.0749527 73
## [195] 1.3807611 126
## [196] 1.4405294 126
## [197] 1.1566116 72
## [198] 1.1044034 75
## [199] 1.0423783 73
## [200] 1.0917192 78
## [201] 1.0402230 78
## [202] 1.0023967 78
## [203] 1.0135506 76
## [204] 1.0962894 76
## [205] 1.1167727 79
## [206] 1.0694466 87
## [207] 1.3918881 128
## [208] 1.4447529 128
## [209] 1.0471380 80
## [210] 1.0638707 73
## [211] 1.0894078 84
## [212] 1.0770563 80
## [213] 1.0691367 90
## [214] 1.0864057 83
## [215] 1.1021572 92
## [216] 1.0952498 86
## [217] 1.3612795 130
## [218] 1.3551198 130
## [219] 1.0115930 78
## [220] 1.0664274 86
## [221] 1.0168262 82
## [222] 1.0078704 77
## [223] 1.3788748 119
## [224] 1.3693344 119
## [225] 1.0542373 88
## [226] 1.0532977 114
## [227] 0.9918255 87
## [228] 1.0131061 86
## [229] 1.0569244 80
## [230] 1.0127544 96
## [231] 1.3743687 140
## [232] 1.3057430 140
## [233] 1.1172287 98
## [234] 1.0720076 91
## [235] 1.0928953 92
## [236] 1.0630545 94
## [237] 1.0703247 106
## [238] 1.0551054 103
## [239] 1.3961841 160
## [240] 1.4106108 160
## [241] 1.0041693 103
## [242] 1.0678548 105
## [243] 1.0947710 103
## [244] 1.2000654 118
## [245] 1.1348912 114
## [246] 1.3874579 159
## [247] 1.3417175 159
## [248] 1.0841422 102
## [249] 1.1575428 113
## [250] 1.0971506 104
## [251] 1.3855509 148
## [252] 1.3449631 148
## [253] 1.1635315 107
## [254] 1.0804221 107
## [255] 1.3773893 152
## [256] 1.3813135 152
## [257] 1.0985487 129
## [258] 1.0248864 116
## [259] 1.3773893 152
## [260] 1.3139323 152
## [261] 1.0854708 129
## [262] 1.4059536 183
## [263] 1.4223272 183
## [264] 1.3895403 173
## [265] 1.3809450 173
## [266] 1.4136364 63
## [267] 1.6417798 63
## [268] 1.3822222 88
## [269] 1.6415055 88
## [270] 1.4136364 74
## [271] 1.5991939 74
## [272] 1.3950359 75
## [273] 1.6012769 75
## [274] 1.3973877 86
## [275] 1.6386974 86
## [276] 1.4136364 77
## [277] 1.5683924 77
## [278] 1.3963969 81
## [279] 1.5948718 81
## [280] 1.3970053 84
## [281] 1.6005882 84
## [282] 1.3791574 80
## [283] 1.5751820 80
## [284] 1.3982708 91
## [285] 1.6288814 91
## [286] 1.3977528 88
## [287] 1.5912553 88
## [288] 1.3928476 67
## [289] 1.5221711 67
## [290] 1.3928476 67
## [291] 1.4304740 67
## [292] 1.3952774 76
## [293] 1.5132367 76
## [294] 1.4136364 66
## [295] 1.4994521 66
## [296] 1.4136364 69
## [297] 1.5477659 69
## [298] 1.3726614 67
## [299] 1.3647051 67
## [300] 1.3701399 63
## [301] 1.3290598 63
## [302] 1.3316864 65
## [303] 1.2798354 65
## [304] 1.3945332 73
## [305] 1.4061068 73
## [306] 1.4136364 63
## [307] 1.2981515 63
## [308] 1.3918881 64
## [309] 1.3830867 64
## [310] 1.4136364 68
## [311] 1.3693344 68
## [312] 1.3803743 83
## [313] 1.2901984 83
## [314] 1.3915483 63
## [315] 1.3955128 63
## [316] 1.3743687 70
## [317] 1.2921415 70
## [318] 1.4136364 74
## [319] 1.3381011 74
## [320] 1.4136364 69
## [321] 1.3436649 69
## [322] 1.3754300 72
## [323] 1.3868450 72
## [324] 1.3952774 76
## [325] 1.4327453 76
## [326] 1.4136364 86
## [327] 1.4377251 86
## [328] 1.4136364 82
## [329] 1.4107820 82
## [330] 1.3769185 75
## [331] 1.2903493 75
## [332] 1.2276316 66
## [333] 1.3778481 77
## [334] 1.2995252 77
## [335] 1.1867565 68
## [336] 1.3764354 74
## [337] 1.2731447 74
## [338] 1.2603505 74
## [339] 1.4136364 89
## [340] 1.3365686 89
## [341] 1.3811390 85
## [342] 1.3212875 85
## [343] 1.3918881 64
## [344] 1.3035989 64
## [345] 1.1948593 71
## [346] 1.3782955 78
## [347] 1.3551198 78
## [348] 1.2440000 66
## [349] 1.3971987 85
## [350] 1.3329804 85
## [351] 1.3782955 78
## [352] 1.3290598 78
## [353] 1.2015909 68
## [354] 1.4136364 89
## [355] 1.4739729 89
## [356] 1.3787318 79
## [357] 1.3083580 79
## [358] 1.0637264 76
## [359] 1.3997772 101
## [360] 1.3874423 101
## [361] 1.3825674 89
## [362] 1.3596129 89
## [363] 1.3999117 102
## [364] 1.4011794 102## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 102
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[7763 item(s), 1020 transaction(s)] done [0.01s].
## sorting and recoding items ... [108 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [310 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
## lhs rhs support confidence
## [1] {structur} => {network} 0.1058824 0.7883212
## [2] {optim} => {network} 0.1088235 0.7449664
## [3] {complex} => {network} 0.1009804 0.7202797
## [4] {input} => {network} 0.1137255 0.7837838
## [5] {learn} => {network} 0.1215686 0.7898089
## [6] {stateoftheart} => {network} 0.1205882 0.7068966
## [7] {classif} => {network} 0.1333333 0.7311828
## [8] {effici} => {network} 0.1294118 0.7252747
## [9] {design} => {network} 0.1313725 0.7322404
## [10] {appli} => {network} 0.1392157 0.7434555
## [11] {set} => {network} 0.1480392 0.7550000
## [12] {architectur} => {network} 0.1558824 0.7535545
## [13] {featur} => {network} 0.1509804 0.7368421
## [14] {inform} => {network} 0.1441176 0.7277228
## [15] {predict} => {network} 0.1558824 0.7098214
## [16] {compar} => {network} 0.1666667 0.7234043
## [17] {accuraci} => {network} 0.1735294 0.7108434
## [18] {problem} => {network} 0.1803922 0.7104247
## [19] {comput} => {network} 0.1911765 0.7303371
## [20] {demonstr} => {network} 0.1911765 0.7303371
## [21] {algorithm} => {network} 0.2000000 0.7285714
## [22] {convolut} => {neural} 0.2666667 0.8802589
## [23] {convolut} => {network} 0.2872549 0.9482201
## [24] {show} => {network} 0.2392157 0.7155425
## [25] {approach} => {network} 0.2509804 0.7191011
## [26] {method} => {network} 0.3176471 0.7089716
## [27] {propos} => {network} 0.3254902 0.7155172
## [28] {train} => {network} 0.3666667 0.7679671
## [29] {neural} => {network} 0.5500000 0.9606164
## [30] {network} => {neural} 0.5500000 0.8106936
## [31] {neural,classif} => {network} 0.1049020 0.9727273
## [32] {network,classif} => {neural} 0.1049020 0.7867647
## [33] {neural,effici} => {network} 0.1058824 0.9557522
## [34] {network,effici} => {neural} 0.1058824 0.8181818
## [35] {neural,design} => {network} 0.1049020 0.9469027
## [36] {network,design} => {neural} 0.1049020 0.7985075
## [37] {appli,neural} => {network} 0.1186275 0.9837398
## [38] {network,appli} => {neural} 0.1186275 0.8521127
## [39] {requir,neural} => {network} 0.1019608 0.9719626
## [40] {network,requir} => {neural} 0.1019608 0.7819549
## [41] {neural,detect} => {network} 0.1078431 0.9649123
## [42] {network,detect} => {neural} 0.1078431 0.8527132
## [43] {neural,larg} => {network} 0.1019608 0.9904762
## [44] {network,larg} => {neural} 0.1019608 0.7878788
## [45] {neural,set} => {network} 0.1215686 0.9687500
## [46] {network,set} => {neural} 0.1215686 0.8211921
## [47] {neural,techniqu} => {network} 0.1029412 0.9633028
## [48] {network,techniqu} => {neural} 0.1029412 0.8015267
## [49] {neural,machin} => {network} 0.1117647 0.9661017
## [50] {network,machin} => {neural} 0.1117647 0.8507463
## [51] {neural,architectur} => {network} 0.1205882 0.9248120
## [52] {network,architectur} => {neural} 0.1205882 0.7735849
## [53] {neural,process} => {network} 0.1058824 0.9391304
## [54] {network,process} => {neural} 0.1058824 0.8059701
## [55] {neural,featur} => {network} 0.1274510 0.9558824
## [56] {network,featur} => {neural} 0.1274510 0.8441558
## [57] {inform,neural} => {network} 0.1166667 0.9754098
## [58] {inform,network} => {neural} 0.1166667 0.8095238
## [59] {neural,task} => {network} 0.1205882 0.9461538
## [60] {network,task} => {neural} 0.1205882 0.8424658
## [61] {neural,signific} => {network} 0.1029412 0.9633028
## [62] {network,signific} => {neural} 0.1029412 0.7954545
## [63] {neural,provid} => {network} 0.1225490 0.9842520
## [64] {network,provid} => {neural} 0.1225490 0.8503401
## [65] {develop,neural} => {network} 0.1009804 0.9363636
## [66] {develop,network} => {neural} 0.1009804 0.8110236
## [67] {neural,studi} => {network} 0.1176471 0.9836066
## [68] {network,studi} => {neural} 0.1176471 0.8510638
## [69] {neural,challeng} => {network} 0.1107843 0.9741379
## [70] {network,challeng} => {neural} 0.1107843 0.7739726
## [71] {neural,time} => {network} 0.1127451 0.9583333
## [72] {network,time} => {neural} 0.1127451 0.7770270
## [73] {neural,predict} => {network} 0.1372549 0.9655172
## [74] {network,predict} => {neural} 0.1372549 0.8805031
## [75] {recent,neural} => {network} 0.1196078 0.9682540
## [76] {network,recent} => {neural} 0.1196078 0.7820513
## [77] {high,neural} => {network} 0.1284314 0.9632353
## [78] {high,network} => {neural} 0.1284314 0.8187500
## [79] {train,compar} => {network} 0.1019608 0.8125000
## [80] {neural,compar} => {network} 0.1303922 0.9637681
## [81] {network,compar} => {neural} 0.1303922 0.7823529
## [82] {neural,work} => {network} 0.1264706 0.9347826
## [83] {network,work} => {neural} 0.1264706 0.7771084
## [84] {neural,accuraci} => {network} 0.1441176 0.9800000
## [85] {network,accuraci} => {neural} 0.1441176 0.8305085
## [86] {problem,data} => {network} 0.1000000 0.7234043
## [87] {neural,problem} => {network} 0.1519608 0.9810127
## [88] {network,problem} => {neural} 0.1519608 0.8423913
## [89] {neural,achiev} => {network} 0.1392157 0.9726027
## [90] {network,achiev} => {neural} 0.1392157 0.8255814
## [91] {neural,applic} => {network} 0.1431373 0.9733333
## [92] {network,applic} => {neural} 0.1431373 0.8742515
## [93] {improv,perform} => {network} 0.1029412 0.7191781
## [94] {propos,improv} => {network} 0.1009804 0.7573529
## [95] {neural,improv} => {network} 0.1362745 0.9720280
## [96] {network,improv} => {neural} 0.1362745 0.7942857
## [97] {neural,present} => {network} 0.1470588 0.9493671
## [98] {network,present} => {neural} 0.1470588 0.8241758
## [99] {system,neural} => {network} 0.1441176 0.9800000
## [100] {network,system} => {neural} 0.1441176 0.8121547
## [101] {propos,comput} => {network} 0.1009804 0.8046875
## [102] {comput,train} => {network} 0.1039216 0.8217054
## [103] {neural,comput} => {network} 0.1450980 0.9610390
## [104] {network,comput} => {neural} 0.1450980 0.7589744
## [105] {demonstr,train} => {network} 0.1058824 0.8307692
## [106] {neural,demonstr} => {network} 0.1558824 0.9636364
## [107] {network,demonstr} => {neural} 0.1558824 0.8153846
## [108] {propos,algorithm} => {network} 0.1009804 0.7518248
## [109] {train,algorithm} => {neural} 0.1009804 0.7054795
## [110] {train,algorithm} => {network} 0.1127451 0.7876712
## [111] {data,algorithm} => {network} 0.1009804 0.7410072
## [112] {neural,algorithm} => {network} 0.1686275 0.9662921
## [113] {network,algorithm} => {neural} 0.1686275 0.8431373
## [114] {dataset,result} => {network} 0.1000000 0.7183099
## [115] {dataset,propos} => {network} 0.1068627 0.7266667
## [116] {dataset,train} => {network} 0.1264706 0.7865854
## [117] {dataset,neural} => {network} 0.1539216 0.9289941
## [118] {dataset,network} => {neural} 0.1539216 0.7850000
## [119] {show,convolut} => {network} 0.1019608 0.9719626
## [120] {convolut,approach} => {neural} 0.1000000 0.9026549
## [121] {convolut,approach} => {network} 0.1049020 0.9469027
## [122] {imag,convolut} => {neural} 0.1323529 0.8709677
## [123] {imag,convolut} => {network} 0.1470588 0.9677419
## [124] {convolut,paper} => {network} 0.1058824 0.9642857
## [125] {convolut,result} => {neural} 0.1215686 0.8857143
## [126] {convolut,result} => {network} 0.1333333 0.9714286
## [127] {convolut,perform} => {neural} 0.1323529 0.9000000
## [128] {convolut,perform} => {network} 0.1401961 0.9533333
## [129] {method,convolut} => {neural} 0.1343137 0.9256757
## [130] {method,convolut} => {network} 0.1382353 0.9527027
## [131] {propos,convolut} => {neural} 0.1284314 0.8851351
## [132] {propos,convolut} => {network} 0.1392157 0.9594595
## [133] {convolut,train} => {neural} 0.1509804 0.9005848
## [134] {convolut,train} => {network} 0.1617647 0.9649123
## [135] {convolut,data} => {neural} 0.1303922 0.9172414
## [136] {convolut,data} => {network} 0.1372549 0.9655172
## [137] {model,convolut} => {neural} 0.1137255 0.8405797
## [138] {model,convolut} => {network} 0.1274510 0.9420290
## [139] {neural,convolut} => {network} 0.2598039 0.9742647
## [140] {network,convolut} => {neural} 0.2598039 0.9044369
## [141] {method,base} => {network} 0.1068627 0.7218543
## [142] {propos,base} => {network} 0.1156863 0.7023810
## [143] {base,train} => {network} 0.1205882 0.7592593
## [144] {neural,base} => {network} 0.1754902 0.9728261
## [145] {network,base} => {neural} 0.1754902 0.8443396
## [146] {show,paper} => {network} 0.1009804 0.7006803
## [147] {show,perform} => {network} 0.1176471 0.7100592
## [148] {method,show} => {network} 0.1225490 0.7812500
## [149] {propos,show} => {network} 0.1284314 0.7485714
## [150] {show,train} => {network} 0.1294118 0.7630058
## [151] {show,data} => {network} 0.1196078 0.7093023
## [152] {model,show} => {network} 0.1205882 0.7109827
## [153] {neural,show} => {network} 0.1990196 0.9712919
## [154] {network,show} => {neural} 0.1990196 0.8319672
## [155] {paper,approach} => {network} 0.1039216 0.7260274
## [156] {result,approach} => {network} 0.1245098 0.7341040
## [157] {perform,approach} => {network} 0.1294118 0.7719298
## [158] {method,approach} => {network} 0.1303922 0.7600000
## [159] {propos,approach} => {network} 0.1401961 0.7566138
## [160] {train,approach} => {network} 0.1382353 0.8150289
## [161] {data,approach} => {network} 0.1333333 0.7234043
## [162] {neural,approach} => {network} 0.2078431 0.9680365
## [163] {network,approach} => {neural} 0.2078431 0.8281250
## [164] {imag,paper} => {network} 0.1009804 0.7202797
## [165] {imag,result} => {network} 0.1235294 0.7368421
## [166] {imag,perform} => {network} 0.1303922 0.7307692
## [167] {imag,method} => {network} 0.1382353 0.7121212
## [168] {imag,propos} => {network} 0.1225490 0.7440476
## [169] {imag,train} => {network} 0.1529412 0.7684729
## [170] {imag,model} => {network} 0.1137255 0.7030303
## [171] {imag,neural} => {network} 0.1980392 0.9573460
## [172] {imag,network} => {neural} 0.1980392 0.7859922
## [173] {paper,result} => {network} 0.1362745 0.7202073
## [174] {paper,perform} => {network} 0.1313725 0.7089947
## [175] {method,paper} => {network} 0.1254902 0.7032967
## [176] {propos,paper} => {network} 0.1421569 0.7107843
## [177] {paper,train} => {network} 0.1196078 0.7484663
## [178] {neural,paper} => {network} 0.2098039 0.9683258
## [179] {network,paper} => {neural} 0.2098039 0.7925926
## [180] {result,perform} => {network} 0.1529412 0.7090909
## [181] {method,result} => {network} 0.1441176 0.7067308
## [182] {propos,result} => {network} 0.1588235 0.7043478
## [183] {result,train} => {network} 0.1696078 0.7757848
## [184] {data,result} => {network} 0.1509804 0.7264151
## [185] {neural,result} => {network} 0.2382353 0.9681275
## [186] {network,result} => {neural} 0.2382353 0.7813505
## [187] {method,perform} => {network} 0.1490196 0.7169811
## [188] {propos,perform} => {network} 0.1509804 0.7162791
## [189] {perform,train} => {network} 0.1715686 0.7510730
## [190] {data,perform} => {network} 0.1666667 0.7083333
## [191] {neural,perform} => {network} 0.2539216 0.9628253
## [192] {network,perform} => {neural} 0.2539216 0.8170347
## [193] {propos,method} => {network} 0.1862745 0.7450980
## [194] {method,train} => {network} 0.1941176 0.7888446
## [195] {method,data} => {network} 0.1637255 0.7106383
## [196] {method,neural} => {network} 0.2578431 0.9633700
## [197] {network,method} => {neural} 0.2578431 0.8117284
## [198] {propos,train} => {network} 0.1784314 0.7777778
## [199] {propos,data} => {network} 0.1666667 0.7391304
## [200] {propos,neural} => {network} 0.2588235 0.9600000
## [201] {network,propos} => {neural} 0.2588235 0.7951807
## [202] {data,train} => {network} 0.1980392 0.7453875
## [203] {model,train} => {network} 0.1813725 0.7283465
## [204] {neural,train} => {network} 0.3009804 0.9715190
## [205] {network,train} => {neural} 0.3009804 0.8208556
## [206] {neural,data} => {network} 0.2735294 0.9687500
## [207] {network,data} => {neural} 0.2735294 0.8110465
## [208] {model,neural} => {network} 0.2696078 0.9649123
## [209] {model,network} => {neural} 0.2696078 0.8184524
## [210] {imag,neural,convolut} => {network} 0.1294118 0.9777778
## [211] {imag,network,convolut} => {neural} 0.1294118 0.8800000
## [212] {neural,convolut,result} => {network} 0.1186275 0.9758065
## [213] {network,convolut,result} => {neural} 0.1186275 0.8897059
## [214] {neural,convolut,perform} => {network} 0.1313725 0.9925926
## [215] {network,convolut,perform} => {neural} 0.1313725 0.9370629
## [216] {method,neural,convolut} => {network} 0.1284314 0.9562044
## [217] {network,method,convolut} => {neural} 0.1284314 0.9290780
## [218] {propos,neural,convolut} => {network} 0.1245098 0.9694656
## [219] {network,propos,convolut} => {neural} 0.1245098 0.8943662
## [220] {neural,convolut,train} => {network} 0.1480392 0.9805195
## [221] {network,convolut,train} => {neural} 0.1480392 0.9151515
## [222] {neural,convolut,data} => {network} 0.1274510 0.9774436
## [223] {network,convolut,data} => {neural} 0.1274510 0.9285714
## [224] {model,neural,convolut} => {network} 0.1117647 0.9827586
## [225] {model,network,convolut} => {neural} 0.1117647 0.8769231
## [226] {neural,base,train} => {network} 0.1058824 0.9729730
## [227] {network,base,train} => {neural} 0.1058824 0.8780488
## [228] {neural,show,perform} => {network} 0.1000000 0.9714286
## [229] {network,show,perform} => {neural} 0.1000000 0.8500000
## [230] {propos,neural,show} => {network} 0.1019608 0.9719626
## [231] {network,propos,show} => {neural} 0.1019608 0.7938931
## [232] {neural,show,train} => {network} 0.1088235 0.9823009
## [233] {network,show,train} => {neural} 0.1088235 0.8409091
## [234] {neural,show,data} => {network} 0.1029412 0.9722222
## [235] {network,show,data} => {neural} 0.1029412 0.8606557
## [236] {model,neural,show} => {network} 0.1019608 0.9719626
## [237] {model,network,show} => {neural} 0.1019608 0.8455285
## [238] {neural,perform,approach} => {network} 0.1078431 0.9821429
## [239] {network,perform,approach} => {neural} 0.1078431 0.8333333
## [240] {method,neural,approach} => {network} 0.1068627 0.9646018
## [241] {network,method,approach} => {neural} 0.1068627 0.8195489
## [242] {propos,neural,approach} => {network} 0.1196078 0.9760000
## [243] {network,propos,approach} => {neural} 0.1196078 0.8531469
## [244] {neural,train,approach} => {network} 0.1137255 0.9747899
## [245] {network,train,approach} => {neural} 0.1137255 0.8226950
## [246] {neural,data,approach} => {network} 0.1127451 0.9663866
## [247] {network,data,approach} => {neural} 0.1127451 0.8455882
## [248] {model,neural,approach} => {network} 0.1009804 0.9537037
## [249] {model,network,approach} => {neural} 0.1009804 0.8442623
## [250] {imag,neural,perform} => {network} 0.1068627 0.9561404
## [251] {imag,network,perform} => {neural} 0.1068627 0.8195489
## [252] {imag,method,neural} => {network} 0.1088235 0.9568966
## [253] {imag,network,method} => {neural} 0.1088235 0.7872340
## [254] {imag,neural,train} => {network} 0.1245098 0.9694656
## [255] {imag,network,train} => {neural} 0.1245098 0.8141026
## [256] {neural,paper,result} => {network} 0.1039216 0.9724771
## [257] {network,paper,result} => {neural} 0.1039216 0.7625899
## [258] {neural,paper,perform} => {network} 0.1039216 0.9724771
## [259] {network,paper,perform} => {neural} 0.1039216 0.7910448
## [260] {propos,neural,paper} => {network} 0.1049020 0.9639640
## [261] {network,propos,paper} => {neural} 0.1049020 0.7379310
## [262] {neural,data,paper} => {network} 0.1009804 0.9626168
## [263] {network,data,paper} => {neural} 0.1009804 0.7984496
## [264] {model,neural,paper} => {network} 0.1058824 0.9642857
## [265] {model,network,paper} => {neural} 0.1058824 0.8181818
## [266] {neural,result,perform} => {network} 0.1215686 0.9612403
## [267] {network,result,perform} => {neural} 0.1215686 0.7948718
## [268] {method,neural,result} => {network} 0.1127451 0.9663866
## [269] {network,method,result} => {neural} 0.1127451 0.7823129
## [270] {propos,neural,result} => {network} 0.1176471 0.9600000
## [271] {network,propos,result} => {neural} 0.1176471 0.7407407
## [272] {neural,result,train} => {network} 0.1362745 0.9788732
## [273] {network,result,train} => {neural} 0.1362745 0.8034682
## [274] {neural,data,result} => {network} 0.1166667 0.9754098
## [275] {network,data,result} => {neural} 0.1166667 0.7727273
## [276] {model,neural,result} => {network} 0.1196078 0.9682540
## [277] {model,network,result} => {neural} 0.1196078 0.7973856
## [278] {method,neural,perform} => {network} 0.1205882 0.9761905
## [279] {network,method,perform} => {neural} 0.1205882 0.8092105
## [280] {propos,neural,perform} => {network} 0.1176471 0.9523810
## [281] {network,propos,perform} => {neural} 0.1176471 0.7792208
## [282] {data,perform,train} => {network} 0.1039216 0.7681159
## [283] {neural,perform,train} => {network} 0.1421569 0.9731544
## [284] {network,perform,train} => {neural} 0.1421569 0.8285714
## [285] {neural,data,perform} => {network} 0.1372549 0.9790210
## [286] {network,data,perform} => {neural} 0.1372549 0.8235294
## [287] {model,neural,perform} => {network} 0.1225490 0.9689922
## [288] {model,network,perform} => {neural} 0.1225490 0.8278146
## [289] {propos,method,train} => {network} 0.1137255 0.8000000
## [290] {propos,method,neural} => {network} 0.1460784 0.9738562
## [291] {network,propos,method} => {neural} 0.1460784 0.7842105
## [292] {method,data,train} => {network} 0.1078431 0.7638889
## [293] {method,neural,train} => {network} 0.1617647 0.9821429
## [294] {network,method,train} => {neural} 0.1617647 0.8333333
## [295] {method,neural,data} => {network} 0.1362745 0.9720280
## [296] {network,method,data} => {neural} 0.1362745 0.8323353
## [297] {model,method,neural} => {network} 0.1245098 0.9694656
## [298] {model,network,method} => {neural} 0.1245098 0.8141026
## [299] {propos,neural,train} => {network} 0.1470588 0.9740260
## [300] {network,propos,train} => {neural} 0.1470588 0.8241758
## [301] {propos,neural,data} => {network} 0.1382353 0.9791667
## [302] {network,propos,data} => {neural} 0.1382353 0.8294118
## [303] {model,propos,neural} => {network} 0.1264706 0.9772727
## [304] {model,network,propos} => {neural} 0.1264706 0.8062500
## [305] {neural,data,train} => {network} 0.1607843 0.9647059
## [306] {network,data,train} => {neural} 0.1607843 0.8118812
## [307] {model,neural,train} => {network} 0.1490196 0.9743590
## [308] {model,network,train} => {neural} 0.1490196 0.8216216
## [309] {model,neural,data} => {network} 0.1421569 0.9666667
## [310] {model,network,data} => {neural} 0.1421569 0.8011050
## lift count
## [1] 1.161976 108
## [2] 1.098072 111
## [3] 1.061684 103
## [4] 1.155288 116
## [5] 1.164169 124
## [6] 1.041957 123
## [7] 1.077755 136
## [8] 1.069047 132
## [9] 1.079314 134
## [10] 1.095845 142
## [11] 1.112861 151
## [12] 1.110731 159
## [13] 1.086097 154
## [14] 1.072655 147
## [15] 1.046269 159
## [16] 1.066290 170
## [17] 1.047775 177
## [18] 1.047158 184
## [19] 1.076508 195
## [20] 1.076508 195
## [21] 1.073906 204
## [22] 1.537438 272
## [23] 1.397665 293
## [24] 1.054701 244
## [25] 1.059947 256
## [26] 1.045016 324
## [27] 1.054664 332
## [28] 1.131975 374
## [29] 1.415938 561
## [30] 1.415938 561
## [31] 1.433789 107
## [32] 1.374144 107
## [33] 1.408768 108
## [34] 1.429016 108
## [35] 1.395724 107
## [36] 1.394653 107
## [37] 1.450021 121
## [38] 1.488279 121
## [39] 1.432662 104
## [40] 1.365743 104
## [41] 1.422270 110
## [42] 1.489328 110
## [43] 1.459950 104
## [44] 1.376090 104
## [45] 1.427926 124
## [46] 1.434274 124
## [47] 1.419897 105
## [48] 1.399927 105
## [49] 1.424023 114
## [50] 1.485892 114
## [51] 1.363162 123
## [52] 1.351124 123
## [53] 1.384267 108
## [54] 1.407688 108
## [55] 1.408960 130
## [56] 1.474382 130
## [57] 1.437743 119
## [58] 1.413894 119
## [59] 1.394620 123
## [60] 1.471430 123
## [61] 1.419897 105
## [62] 1.389321 105
## [63] 1.450776 125
## [64] 1.485183 125
## [65] 1.380189 103
## [66] 1.416514 103
## [67] 1.449825 120
## [68] 1.486447 120
## [69] 1.435868 113
## [70] 1.351801 113
## [71] 1.412572 115
## [72] 1.357136 115
## [73] 1.423161 140
## [74] 1.537865 140
## [75] 1.427195 122
## [76] 1.365911 122
## [77] 1.419798 131
## [78] 1.430009 131
## [79] 1.197616 104
## [80] 1.420583 133
## [81] 1.366438 133
## [82] 1.377859 129
## [83] 1.357278 129
## [84] 1.444509 147
## [85] 1.450546 147
## [86] 1.066290 102
## [87] 1.446001 155
## [88] 1.471300 155
## [89] 1.433605 142
## [90] 1.441940 142
## [91] 1.434682 146
## [92] 1.526946 146
## [93] 1.060060 105
## [94] 1.116329 103
## [95] 1.432758 139
## [96] 1.387280 139
## [97] 1.399356 150
## [98] 1.439485 150
## [99] 1.444509 147
## [100] 1.418489 147
## [101] 1.186100 103
## [102] 1.211184 106
## [103] 1.416560 148
## [104] 1.325606 148
## [105] 1.224544 108
## [106] 1.420389 159
## [107] 1.424131 159
## [108] 1.108181 103
## [109] 1.232173 103
## [110] 1.161018 115
## [111] 1.092236 103
## [112] 1.424303 172
## [113] 1.472603 172
## [114] 1.058780 102
## [115] 1.071098 109
## [116] 1.159418 129
## [117] 1.369327 157
## [118] 1.371062 157
## [119] 1.432662 104
## [120] 1.576555 102
## [121] 1.395724 107
## [122] 1.521211 135
## [123] 1.426440 150
## [124] 1.421346 108
## [125] 1.546967 124
## [126] 1.431874 136
## [127] 1.571918 135
## [128] 1.405202 143
## [129] 1.616762 137
## [130] 1.404273 141
## [131] 1.545955 131
## [132] 1.414232 142
## [133] 1.572939 154
## [134] 1.422270 165
## [135] 1.602031 133
## [136] 1.423161 140
## [137] 1.468136 116
## [138] 1.388540 130
## [139] 1.436055 265
## [140] 1.579667 265
## [141] 1.064005 109
## [142] 1.035301 118
## [143] 1.119139 123
## [144] 1.433934 179
## [145] 1.474703 179
## [146] 1.032795 103
## [147] 1.046619 120
## [148] 1.151553 125
## [149] 1.103386 131
## [150] 1.124662 132
## [151] 1.045503 122
## [152] 1.047980 123
## [153] 1.431673 203
## [154] 1.453093 203
## [155] 1.070156 106
## [156] 1.082061 127
## [157] 1.137816 132
## [158] 1.120231 133
## [159] 1.115240 143
## [160] 1.201343 141
## [161] 1.066290 136
## [162] 1.426875 212
## [163] 1.446383 212
## [164] 1.061684 103
## [165] 1.086097 126
## [166] 1.077145 133
## [167] 1.049658 141
## [168] 1.096718 125
## [169] 1.132720 156
## [170] 1.036259 116
## [171] 1.411117 202
## [172] 1.372795 202
## [173] 1.061577 139
## [174] 1.045050 134
## [175] 1.036651 128
## [176] 1.047688 145
## [177] 1.103231 122
## [178] 1.427301 214
## [179] 1.384323 214
## [180] 1.045192 156
## [181] 1.041713 147
## [182] 1.038201 162
## [183] 1.143498 173
## [184] 1.070727 154
## [185] 1.427009 243
## [186] 1.364687 243
## [187] 1.056822 152
## [188] 1.055787 154
## [189] 1.107073 175
## [190] 1.044075 170
## [191] 1.419193 259
## [192] 1.427013 259
## [193] 1.098266 190
## [194] 1.162748 198
## [195] 1.047473 167
## [196] 1.419996 263
## [197] 1.417745 263
## [198] 1.146435 182
## [199] 1.089470 170
## [200] 1.415029 264
## [201] 1.388843 264
## [202] 1.098692 202
## [203] 1.073574 185
## [204] 1.432008 307
## [205] 1.433686 307
## [206] 1.427926 279
## [207] 1.416554 279
## [208] 1.422270 275
## [209] 1.429489 275
## [210] 1.441233 132
## [211] 1.536986 132
## [212] 1.438327 121
## [213] 1.553938 121
## [214] 1.463070 134
## [215] 1.636651 134
## [216] 1.409434 131
## [217] 1.622705 131
## [218] 1.428981 127
## [219] 1.562078 127
## [220] 1.445274 151
## [221] 1.598381 151
## [222] 1.440741 130
## [223] 1.621820 130
## [224] 1.448575 114
## [225] 1.531612 114
## [226] 1.434151 108
## [227] 1.533578 108
## [228] 1.431874 102
## [229] 1.484589 102
## [230] 1.432662 104
## [231] 1.386594 104
## [232] 1.447900 111
## [233] 1.468711 111
## [234] 1.433044 105
## [235] 1.503200 105
## [236] 1.432662 104
## [237] 1.476779 104
## [238] 1.447667 110
## [239] 1.455479 110
## [240] 1.421812 109
## [241] 1.431404 109
## [242] 1.438613 122
## [243] 1.490085 122
## [244] 1.436829 116
## [245] 1.436899 116
## [246] 1.424443 115
## [247] 1.476884 115
## [248] 1.405748 103
## [249] 1.474568 103
## [250] 1.409340 109
## [251] 1.431404 109
## [252] 1.410454 111
## [253] 1.374964 111
## [254] 1.428981 127
## [255] 1.421891 127
## [256] 1.433420 106
## [257] 1.331921 106
## [258] 1.433420 106
## [259] 1.381619 106
## [260] 1.420872 107
## [261] 1.288852 107
## [262] 1.418886 103
## [263] 1.394552 103
## [264] 1.421346 108
## [265] 1.429016 108
## [266] 1.416857 124
## [267] 1.388303 124
## [268] 1.424443 115
## [269] 1.366368 115
## [270] 1.415029 120
## [271] 1.293760 120
## [272] 1.442848 139
## [273] 1.403318 139
## [274] 1.437743 119
## [275] 1.349626 119
## [276] 1.427195 122
## [277] 1.392694 122
## [278] 1.438893 123
## [279] 1.413347 123
## [280] 1.403799 120
## [281] 1.360968 120
## [282] 1.132194 106
## [283] 1.434418 145
## [284] 1.447162 145
## [285] 1.443066 140
## [286] 1.438356 140
## [287] 1.428283 125
## [288] 1.445841 125
## [289] 1.179191 116
## [290] 1.435453 149
## [291] 1.369683 149
## [292] 1.125963 110
## [293] 1.447667 165
## [294] 1.455479 165
## [295] 1.432758 139
## [296] 1.453736 139
## [297] 1.428981 127
## [298] 1.421891 127
## [299] 1.435703 150
## [300] 1.439485 150
## [301] 1.443280 141
## [302] 1.448630 141
## [303] 1.440489 129
## [304] 1.408176 129
## [305] 1.421965 164
## [306] 1.418012 164
## [307] 1.436194 152
## [308] 1.435024 152
## [309] 1.424855 145
## [310] 1.399190 145## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 202
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[11053 item(s), 2027 transaction(s)] done [0.01s].
## sorting and recoding items ... [106 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [349 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
## lhs rhs support confidence
## [1] {cnn} => {neural} 0.1006413 0.8908297
## [2] {cnn} => {network} 0.1060681 0.9388646
## [3] {layer} => {network} 0.1080414 0.8390805
## [4] {general} => {network} 0.1031080 0.7491039
## [5] {function} => {network} 0.1006413 0.7391304
## [6] {input} => {network} 0.1159349 0.8131488
## [7] {complex} => {network} 0.1050814 0.7421603
## [8] {number} => {network} 0.1045881 0.7138047
## [9] {optim} => {network} 0.1124815 0.7549669
## [10] {test} => {network} 0.1149482 0.7147239
## [11] {design} => {network} 0.1188949 0.7259036
## [12] {effici} => {network} 0.1262950 0.7485380
## [13] {appli} => {network} 0.1346818 0.7438692
## [14] {stateoftheart} => {network} 0.1327084 0.7270270
## [15] {learn} => {network} 0.1356685 0.7452575
## [16] {larg} => {network} 0.1332018 0.7031250
## [17] {set} => {network} 0.1484953 0.7678571
## [18] {inform} => {network} 0.1366552 0.7139175
## [19] {studi} => {network} 0.1406019 0.7107232
## [20] {time} => {network} 0.1440553 0.7373737
## [21] {classif} => {network} 0.1593488 0.7511628
## [22] {architectur} => {network} 0.1741490 0.7775330
## [23] {predict} => {network} 0.1593488 0.7258427
## [24] {compar} => {network} 0.1618155 0.7404063
## [25] {task} => {network} 0.1682289 0.7002053
## [26] {problem} => {network} 0.1736556 0.7082495
## [27] {accuraci} => {network} 0.1780957 0.7307692
## [28] {featur} => {network} 0.1854958 0.7387033
## [29] {achiev} => {network} 0.1859891 0.7208413
## [30] {demonstr} => {network} 0.1874692 0.7265774
## [31] {algorithm} => {network} 0.1958559 0.7179024
## [32] {comput} => {network} 0.2017760 0.7316637
## [33] {dataset} => {network} 0.2022694 0.7032590
## [34] {convolut} => {neural} 0.2738037 0.8604651
## [35] {convolut} => {network} 0.3034040 0.9534884
## [36] {imag} => {network} 0.2580168 0.7294282
## [37] {show} => {network} 0.2639369 0.7338820
## [38] {approach} => {network} 0.2570301 0.7117486
## [39] {method} => {network} 0.3108041 0.7110609
## [40] {result} => {network} 0.3117908 0.7117117
## [41] {perform} => {network} 0.3152442 0.7092120
## [42] {propos} => {network} 0.3260977 0.7344444
## [43] {train} => {network} 0.3660582 0.7713098
## [44] {data} => {network} 0.3285644 0.7040169
## [45] {neural} => {network} 0.5510607 0.9679376
## [46] {network} => {neural} 0.5510607 0.7933239
## [47] {appli,neural} => {network} 0.1105081 0.9824561
## [48] {network,appli} => {neural} 0.1105081 0.8205128
## [49] {neural,learn} => {network} 0.1040947 0.9723502
## [50] {network,learn} => {neural} 0.1040947 0.7672727
## [51] {neural,detect} => {network} 0.1016280 0.9716981
## [52] {network,detect} => {neural} 0.1016280 0.8207171
## [53] {neural,experi} => {network} 0.1050814 0.9770642
## [54] {network,experi} => {neural} 0.1050814 0.8129771
## [55] {evalu,neural} => {network} 0.1001480 0.9712919
## [56] {evalu,network} => {neural} 0.1001480 0.7518519
## [57] {neural,larg} => {network} 0.1050814 0.9953271
## [58] {network,larg} => {neural} 0.1050814 0.7888889
## [59] {neural,set} => {network} 0.1149482 0.9708333
## [60] {network,set} => {neural} 0.1149482 0.7740864
## [61] {inform,neural} => {network} 0.1100148 0.9695652
## [62] {inform,network} => {neural} 0.1100148 0.8050542
## [63] {neural,provid} => {network} 0.1075481 0.9819820
## [64] {network,provid} => {neural} 0.1075481 0.8074074
## [65] {neural,techniqu} => {network} 0.1050814 0.9770642
## [66] {network,techniqu} => {neural} 0.1050814 0.8007519
## [67] {neural,studi} => {network} 0.1154415 0.9831933
## [68] {network,studi} => {neural} 0.1154415 0.8210526
## [69] {neural,time} => {network} 0.1075481 0.9688889
## [70] {network,time} => {neural} 0.1075481 0.7465753
## [71] {neural,signific} => {network} 0.1045881 0.9680365
## [72] {network,signific} => {neural} 0.1045881 0.7940075
## [73] {neural,classif} => {network} 0.1272817 0.9699248
## [74] {network,classif} => {neural} 0.1272817 0.7987616
## [75] {neural,process} => {network} 0.1139615 0.9585062
## [76] {network,process} => {neural} 0.1139615 0.8133803
## [77] {develop,neural} => {network} 0.1065614 0.9600000
## [78] {develop,network} => {neural} 0.1065614 0.8089888
## [79] {neural,machin} => {network} 0.1208683 0.9760956
## [80] {network,machin} => {neural} 0.1208683 0.8390411
## [81] {neural,challeng} => {network} 0.1105081 0.9781659
## [82] {network,challeng} => {neural} 0.1105081 0.7724138
## [83] {neural,architectur} => {network} 0.1287617 0.9490909
## [84] {network,architectur} => {neural} 0.1287617 0.7393768
## [85] {recent,neural} => {network} 0.1134682 0.9745763
## [86] {network,recent} => {neural} 0.1134682 0.7718121
## [87] {model,predict} => {network} 0.1011347 0.7044674
## [88] {neural,predict} => {network} 0.1317218 0.9744526
## [89] {network,predict} => {neural} 0.1317218 0.8266254
## [90] {neural,compar} => {network} 0.1223483 0.9725490
## [91] {network,compar} => {neural} 0.1223483 0.7560976
## [92] {high,neural} => {network} 0.1203749 0.9721116
## [93] {high,network} => {neural} 0.1203749 0.8000000
## [94] {neural,work} => {network} 0.1312284 0.9568345
## [95] {network,work} => {neural} 0.1312284 0.7800587
## [96] {neural,task} => {network} 0.1366552 0.9651568
## [97] {network,task} => {neural} 0.1366552 0.8123167
## [98] {neural,problem} => {network} 0.1406019 0.9793814
## [99] {network,problem} => {neural} 0.1406019 0.8096591
## [100] {neural,applic} => {network} 0.1361618 0.9857143
## [101] {network,applic} => {neural} 0.1361618 0.8440367
## [102] {model,accuraci} => {network} 0.1001480 0.7463235
## [103] {neural,accuraci} => {network} 0.1425752 0.9796610
## [104] {network,accuraci} => {neural} 0.1425752 0.8005540
## [105] {improv,perform} => {network} 0.1070548 0.7508651
## [106] {neural,improv} => {network} 0.1336951 0.9575972
## [107] {network,improv} => {neural} 0.1336951 0.7655367
## [108] {neural,featur} => {network} 0.1504687 0.9744409
## [109] {network,featur} => {neural} 0.1504687 0.8111702
## [110] {neural,present} => {network} 0.1475086 0.9676375
## [111] {network,present} => {neural} 0.1475086 0.8037634
## [112] {perform,achiev} => {network} 0.1031080 0.7256944
## [113] {neural,achiev} => {network} 0.1475086 0.9676375
## [114] {network,achiev} => {neural} 0.1475086 0.7931034
## [115] {system,neural} => {network} 0.1396152 0.9725086
## [116] {network,system} => {neural} 0.1396152 0.8108883
## [117] {demonstr,train} => {network} 0.1040947 0.7992424
## [118] {neural,demonstr} => {network} 0.1480020 0.9646302
## [119] {network,demonstr} => {neural} 0.1480020 0.7894737
## [120] {train,algorithm} => {network} 0.1105081 0.7915194
## [121] {neural,algorithm} => {network} 0.1558954 0.9604863
## [122] {network,algorithm} => {neural} 0.1558954 0.7959698
## [123] {propos,comput} => {network} 0.1036014 0.8076923
## [124] {comput,train} => {network} 0.1080414 0.8081181
## [125] {model,comput} => {network} 0.1050814 0.7500000
## [126] {neural,comput} => {network} 0.1563888 0.9753846
## [127] {network,comput} => {neural} 0.1563888 0.7750611
## [128] {dataset,convolut} => {network} 0.1065614 0.9515419
## [129] {dataset,result} => {network} 0.1036014 0.7317073
## [130] {dataset,propos} => {network} 0.1100148 0.7335526
## [131] {dataset,train} => {network} 0.1253083 0.7627628
## [132] {dataset,data} => {network} 0.1026147 0.7098976
## [133] {dataset,neural} => {network} 0.1578688 0.9495549
## [134] {dataset,network} => {neural} 0.1578688 0.7804878
## [135] {convolut,base} => {neural} 0.1026147 0.8776371
## [136] {convolut,base} => {network} 0.1129748 0.9662447
## [137] {base,approach} => {network} 0.1006413 0.7527675
## [138] {method,base} => {network} 0.1100148 0.7263844
## [139] {base,perform} => {network} 0.1045881 0.7090301
## [140] {propos,base} => {network} 0.1159349 0.7253086
## [141] {base,train} => {network} 0.1223483 0.7750000
## [142] {data,base} => {network} 0.1085348 0.7284768
## [143] {neural,base} => {network} 0.1835224 0.9738220
## [144] {network,base} => {neural} 0.1835224 0.8104575
## [145] {imag,convolut} => {neural} 0.1425752 0.8704819
## [146] {imag,convolut} => {network} 0.1573754 0.9608434
## [147] {show,convolut} => {neural} 0.1016280 0.8512397
## [148] {show,convolut} => {network} 0.1149482 0.9628099
## [149] {convolut,approach} => {neural} 0.1090281 0.8911290
## [150] {convolut,approach} => {network} 0.1169216 0.9556452
## [151] {convolut,paper} => {neural} 0.1110015 0.8587786
## [152] {convolut,paper} => {network} 0.1253083 0.9694656
## [153] {method,convolut} => {neural} 0.1341885 0.8918033
## [154] {method,convolut} => {network} 0.1455353 0.9672131
## [155] {convolut,result} => {neural} 0.1302417 0.8712871
## [156] {convolut,result} => {network} 0.1450419 0.9702970
## [157] {convolut,perform} => {neural} 0.1332018 0.8598726
## [158] {convolut,perform} => {network} 0.1484953 0.9585987
## [159] {propos,convolut} => {neural} 0.1272817 0.8686869
## [160] {propos,convolut} => {network} 0.1420819 0.9696970
## [161] {convolut,train} => {neural} 0.1475086 0.8742690
## [162] {convolut,train} => {network} 0.1608288 0.9532164
## [163] {convolut,data} => {neural} 0.1302417 0.8918919
## [164] {convolut,data} => {network} 0.1410952 0.9662162
## [165] {model,convolut} => {neural} 0.1277750 0.8519737
## [166] {model,convolut} => {network} 0.1410952 0.9407895
## [167] {neural,convolut} => {network} 0.2683769 0.9801802
## [168] {network,convolut} => {neural} 0.2683769 0.8845528
## [169] {imag,paper} => {network} 0.1090281 0.7517007
## [170] {imag,method} => {network} 0.1386285 0.7356021
## [171] {imag,result} => {network} 0.1277750 0.7529070
## [172] {imag,perform} => {network} 0.1312284 0.7643678
## [173] {imag,propos} => {network} 0.1267884 0.7740964
## [174] {imag,train} => {network} 0.1524420 0.7763819
## [175] {imag,data} => {network} 0.1164282 0.7217125
## [176] {imag,model} => {network} 0.1149482 0.7236025
## [177] {imag,neural} => {network} 0.2057227 0.9652778
## [178] {imag,network} => {neural} 0.2057227 0.7973231
## [179] {show,paper} => {network} 0.1164282 0.7173252
## [180] {method,show} => {network} 0.1253083 0.7537092
## [181] {show,result} => {network} 0.1361618 0.7168831
## [182] {show,perform} => {network} 0.1233350 0.7267442
## [183] {propos,show} => {network} 0.1327084 0.7472222
## [184] {show,train} => {network} 0.1396152 0.7669377
## [185] {show,data} => {network} 0.1307351 0.7402235
## [186] {model,show} => {network} 0.1322151 0.7165775
## [187] {neural,show} => {network} 0.2116428 0.9683973
## [188] {network,show} => {neural} 0.2116428 0.8018692
## [189] {paper,approach} => {network} 0.1114948 0.7243590
## [190] {method,approach} => {network} 0.1248150 0.7249284
## [191] {result,approach} => {network} 0.1233350 0.7246377
## [192] {perform,approach} => {network} 0.1277750 0.7400000
## [193] {propos,approach} => {network} 0.1440553 0.7584416
## [194] {train,approach} => {network} 0.1366552 0.7847025
## [195] {data,approach} => {network} 0.1336951 0.7226667
## [196] {neural,approach} => {network} 0.2062161 0.9720930
## [197] {network,approach} => {neural} 0.2062161 0.8023033
## [198] {method,paper} => {network} 0.1277750 0.7076503
## [199] {paper,result} => {network} 0.1425752 0.7261307
## [200] {paper,perform} => {network} 0.1351751 0.7154047
## [201] {propos,paper} => {network} 0.1598421 0.7346939
## [202] {paper,train} => {network} 0.1312284 0.7665706
## [203] {neural,paper} => {network} 0.2215096 0.9655914
## [204] {network,paper} => {neural} 0.2215096 0.7701544
## [205] {method,perform} => {network} 0.1430686 0.7196030
## [206] {propos,method} => {network} 0.1726690 0.7322176
## [207] {method,train} => {network} 0.1830291 0.7961373
## [208] {method,data} => {network} 0.1583621 0.7345538
## [209] {method,neural} => {network} 0.2461766 0.9708171
## [210] {network,method} => {neural} 0.2461766 0.7920635
## [211] {result,perform} => {network} 0.1484953 0.7305825
## [212] {propos,result} => {network} 0.1593488 0.7291196
## [213] {result,train} => {network} 0.1692156 0.7903226
## [214] {data,result} => {network} 0.1470153 0.7215496
## [215] {neural,result} => {network} 0.2442033 0.9705882
## [216] {network,result} => {neural} 0.2442033 0.7832278
## [217] {propos,perform} => {network} 0.1529354 0.7345972
## [218] {perform,train} => {network} 0.1731623 0.7597403
## [219] {data,perform} => {network} 0.1568821 0.7243736
## [220] {neural,perform} => {network} 0.2520967 0.9714829
## [221] {network,perform} => {neural} 0.2520967 0.7996870
## [222] {propos,train} => {network} 0.1776024 0.7947020
## [223] {propos,data} => {network} 0.1628022 0.7568807
## [224] {model,propos} => {network} 0.1637889 0.7217391
## [225] {propos,neural} => {network} 0.2535767 0.9679849
## [226] {network,propos} => {neural} 0.2535767 0.7776097
## [227] {data,train} => {network} 0.1948693 0.7596154
## [228] {model,train} => {network} 0.1869758 0.7373541
## [229] {neural,train} => {network} 0.2955106 0.9771615
## [230] {network,train} => {neural} 0.2955106 0.8072776
## [231] {neural,data} => {network} 0.2580168 0.9721190
## [232] {network,data} => {neural} 0.2580168 0.7852853
## [233] {model,neural} => {network} 0.2747903 0.9686957
## [234] {model,network} => {neural} 0.2747903 0.8084180
## [235] {dataset,neural,train} => {network} 0.1011347 0.9715640
## [236] {dataset,network,train} => {neural} 0.1011347 0.8070866
## [237] {neural,convolut,base} => {network} 0.1011347 0.9855769
## [238] {network,convolut,base} => {neural} 0.1011347 0.8951965
## [239] {neural,base,train} => {network} 0.1036014 0.9813084
## [240] {network,base,train} => {neural} 0.1036014 0.8467742
## [241] {imag,neural,convolut} => {network} 0.1391219 0.9757785
## [242] {imag,network,convolut} => {neural} 0.1391219 0.8840125
## [243] {neural,convolut,approach} => {network} 0.1055747 0.9683258
## [244] {network,convolut,approach} => {neural} 0.1055747 0.9029536
## [245] {neural,convolut,paper} => {network} 0.1100148 0.9911111
## [246] {network,convolut,paper} => {neural} 0.1100148 0.8779528
## [247] {method,neural,convolut} => {network} 0.1307351 0.9742647
## [248] {network,method,convolut} => {neural} 0.1307351 0.8983051
## [249] {neural,convolut,result} => {network} 0.1277750 0.9810606
## [250] {network,convolut,result} => {neural} 0.1277750 0.8809524
## [251] {neural,convolut,perform} => {network} 0.1322151 0.9925926
## [252] {network,convolut,perform} => {neural} 0.1322151 0.8903654
## [253] {propos,neural,convolut} => {network} 0.1248150 0.9806202
## [254] {network,propos,convolut} => {neural} 0.1248150 0.8784722
## [255] {neural,convolut,train} => {network} 0.1445486 0.9799331
## [256] {network,convolut,train} => {neural} 0.1445486 0.8987730
## [257] {neural,convolut,data} => {network} 0.1277750 0.9810606
## [258] {network,convolut,data} => {neural} 0.1277750 0.9055944
## [259] {model,neural,convolut} => {network} 0.1258017 0.9845560
## [260] {model,network,convolut} => {neural} 0.1258017 0.8916084
## [261] {imag,method,neural} => {network} 0.1105081 0.9696970
## [262] {imag,network,method} => {neural} 0.1105081 0.7971530
## [263] {imag,neural,result} => {network} 0.1001480 0.9712919
## [264] {imag,network,result} => {neural} 0.1001480 0.7837838
## [265] {imag,neural,perform} => {network} 0.1075481 0.9688889
## [266] {imag,network,perform} => {neural} 0.1075481 0.8195489
## [267] {imag,neural,train} => {network} 0.1262950 0.9696970
## [268] {imag,network,train} => {neural} 0.1262950 0.8284790
## [269] {neural,show,result} => {network} 0.1065614 0.9557522
## [270] {network,show,result} => {neural} 0.1065614 0.7826087
## [271] {neural,show,perform} => {network} 0.1006413 0.9714286
## [272] {network,show,perform} => {neural} 0.1006413 0.8160000
## [273] {propos,neural,show} => {network} 0.1036014 0.9677419
## [274] {network,propos,show} => {neural} 0.1036014 0.7806691
## [275] {neural,show,train} => {network} 0.1129748 0.9786325
## [276] {network,show,train} => {neural} 0.1129748 0.8091873
## [277] {neural,show,data} => {network} 0.1031080 0.9675926
## [278] {network,show,data} => {neural} 0.1031080 0.7886792
## [279] {model,neural,show} => {network} 0.1036014 0.9633028
## [280] {model,network,show} => {neural} 0.1036014 0.7835821
## [281] {method,neural,approach} => {network} 0.1001480 0.9712919
## [282] {network,method,approach} => {neural} 0.1001480 0.8023715
## [283] {neural,perform,approach} => {network} 0.1026147 0.9857820
## [284] {network,perform,approach} => {neural} 0.1026147 0.8030888
## [285] {propos,neural,approach} => {network} 0.1144549 0.9789030
## [286] {network,propos,approach} => {neural} 0.1144549 0.7945205
## [287] {neural,train,approach} => {network} 0.1090281 0.9735683
## [288] {network,train,approach} => {neural} 0.1090281 0.7978339
## [289] {neural,data,approach} => {network} 0.1055747 0.9683258
## [290] {network,data,approach} => {neural} 0.1055747 0.7896679
## [291] {model,neural,approach} => {network} 0.1011347 0.9669811
## [292] {model,network,approach} => {neural} 0.1011347 0.8102767
## [293] {neural,paper,result} => {network} 0.1070548 0.9774775
## [294] {network,paper,result} => {neural} 0.1070548 0.7508651
## [295] {neural,paper,perform} => {network} 0.1050814 0.9726027
## [296] {network,paper,perform} => {neural} 0.1050814 0.7773723
## [297] {propos,neural,paper} => {network} 0.1184016 0.9716599
## [298] {network,propos,paper} => {neural} 0.1184016 0.7407407
## [299] {neural,paper,train} => {network} 0.1026147 0.9765258
## [300] {network,paper,train} => {neural} 0.1026147 0.7819549
## [301] {neural,data,paper} => {network} 0.1026147 0.9629630
## [302] {network,data,paper} => {neural} 0.1026147 0.7647059
## [303] {model,neural,paper} => {network} 0.1119882 0.9578059
## [304] {model,network,paper} => {neural} 0.1119882 0.8021201
## [305] {method,neural,result} => {network} 0.1090281 0.9692982
## [306] {network,method,result} => {neural} 0.1090281 0.7700348
## [307] {method,neural,perform} => {network} 0.1124815 0.9827586
## [308] {network,method,perform} => {neural} 0.1124815 0.7862069
## [309] {propos,method,train} => {network} 0.1021214 0.7992278
## [310] {propos,method,neural} => {network} 0.1332018 0.9747292
## [311] {network,propos,method} => {neural} 0.1332018 0.7714286
## [312] {method,data,train} => {network} 0.1045881 0.8000000
## [313] {method,neural,train} => {network} 0.1475086 0.9835526
## [314] {network,method,train} => {neural} 0.1475086 0.8059299
## [315] {method,neural,data} => {network} 0.1277750 0.9736842
## [316] {network,method,data} => {neural} 0.1277750 0.8068536
## [317] {model,method,neural} => {network} 0.1218550 0.9686275
## [318] {model,network,method} => {neural} 0.1218550 0.7967742
## [319] {neural,result,perform} => {network} 0.1179082 0.9676113
## [320] {network,result,perform} => {neural} 0.1179082 0.7940199
## [321] {propos,neural,result} => {network} 0.1193883 0.9641434
## [322] {network,propos,result} => {neural} 0.1193883 0.7492260
## [323] {neural,result,train} => {network} 0.1371485 0.9823322
## [324] {network,result,train} => {neural} 0.1371485 0.8104956
## [325] {neural,data,result} => {network} 0.1114948 0.9741379
## [326] {network,data,result} => {neural} 0.1114948 0.7583893
## [327] {model,neural,result} => {network} 0.1218550 0.9686275
## [328] {model,network,result} => {neural} 0.1218550 0.7891374
## [329] {propos,neural,perform} => {network} 0.1164282 0.9711934
## [330] {network,propos,perform} => {neural} 0.1164282 0.7612903
## [331] {neural,perform,train} => {network} 0.1406019 0.9827586
## [332] {network,perform,train} => {neural} 0.1406019 0.8119658
## [333] {neural,data,perform} => {network} 0.1287617 0.9812030
## [334] {network,data,perform} => {neural} 0.1287617 0.8207547
## [335] {model,neural,perform} => {network} 0.1253083 0.9731801
## [336] {model,network,perform} => {neural} 0.1253083 0.8246753
## [337] {propos,neural,train} => {network} 0.1415886 0.9761905
## [338] {network,propos,train} => {neural} 0.1415886 0.7972222
## [339] {propos,neural,data} => {network} 0.1277750 0.9736842
## [340] {network,propos,data} => {neural} 0.1277750 0.7848485
## [341] {model,propos,neural} => {network} 0.1297484 0.9740741
## [342] {model,network,propos} => {neural} 0.1297484 0.7921687
## [343] {model,data,train} => {network} 0.1055747 0.7157191
## [344] {neural,data,train} => {network} 0.1578688 0.9726444
## [345] {network,data,train} => {neural} 0.1578688 0.8101266
## [346] {model,neural,train} => {network} 0.1519487 0.9746835
## [347] {model,network,train} => {neural} 0.1519487 0.8126649
## [348] {model,neural,data} => {network} 0.1371485 0.9686411
## [349] {model,network,data} => {neural} 0.1371485 0.7853107
## lift count
## [1] 1.564742 204
## [2] 1.351618 215
## [3] 1.207966 219
## [4] 1.078433 209
## [5] 1.064075 204
## [6] 1.170634 235
## [7] 1.068437 213
## [8] 1.027615 212
## [9] 1.086873 228
## [10] 1.028938 233
## [11] 1.045033 241
## [12] 1.077618 256
## [13] 1.070897 273
## [14] 1.046650 269
## [15] 1.072895 275
## [16] 1.012240 270
## [17] 1.105431 301
## [18] 1.027778 277
## [19] 1.023179 285
## [20] 1.061546 292
## [21] 1.081397 323
## [22] 1.119360 353
## [23] 1.044945 323
## [24] 1.065912 328
## [25] 1.008037 341
## [26] 1.019618 352
## [27] 1.052038 361
## [28] 1.063460 376
## [29] 1.037745 377
## [30] 1.046003 380
## [31] 1.033514 397
## [32] 1.053325 409
## [33] 1.012433 410
## [34] 1.511406 555
## [35] 1.372671 615
## [36] 1.050107 523
## [37] 1.056519 535
## [38] 1.024655 521
## [39] 1.023665 630
## [40] 1.024602 632
## [41] 1.021003 639
## [42] 1.057329 661
## [43] 1.110401 742
## [44] 1.013524 666
## [45] 1.393473 1117
## [46] 1.393473 1117
## [47] 1.414374 224
## [48] 1.441230 224
## [49] 1.399825 211
## [50] 1.347714 211
## [51] 1.398886 206
## [52] 1.441589 206
## [53] 1.406612 213
## [54] 1.427994 213
## [55] 1.398302 203
## [56] 1.320627 203
## [57] 1.432903 213
## [58] 1.385683 213
## [59] 1.397641 233
## [60] 1.359682 233
## [61] 1.395816 223
## [62] 1.414077 223
## [63] 1.413691 218
## [64] 1.418210 218
## [65] 1.406612 213
## [66] 1.406520 213
## [67] 1.415435 234
## [68] 1.442178 234
## [69] 1.394842 218
## [70] 1.311359 218
## [71] 1.393615 212
## [72] 1.394673 212
## [73] 1.396334 258
## [74] 1.403024 258
## [75] 1.379895 231
## [76] 1.428702 231
## [77] 1.382045 216
## [78] 1.420988 216
## [79] 1.405217 245
## [80] 1.473775 245
## [81] 1.408198 224
## [82] 1.356744 224
## [83] 1.366340 261
## [84] 1.298715 261
## [85] 1.403030 230
## [86] 1.355687 230
## [87] 1.014173 205
## [88] 1.402852 267
## [89] 1.451967 267
## [90] 1.400111 248
## [91] 1.328085 248
## [92] 1.399482 244
## [93] 1.405199 244
## [94] 1.377488 266
## [95] 1.370172 266
## [96] 1.389469 277
## [97] 1.426834 277
## [98] 1.409948 285
## [99] 1.422165 285
## [100] 1.419065 276
## [101] 1.482550 276
## [102] 1.074430 203
## [103] 1.410350 289
## [104] 1.406172 289
## [105] 1.080968 217
## [106] 1.378586 271
## [107] 1.344665 271
## [108] 1.402835 305
## [109] 1.424820 305
## [110] 1.393041 299
## [111] 1.411810 299
## [112] 1.044732 209
## [113] 1.393041 299
## [114] 1.393086 299
## [115] 1.400053 283
## [116] 1.424325 283
## [117] 1.150614 211
## [118] 1.388711 300
## [119] 1.386710 300
## [120] 1.139496 224
## [121] 1.382746 316
## [122] 1.398120 316
## [123] 1.162779 210
## [124] 1.163392 219
## [125] 1.079723 213
## [126] 1.404194 317
## [127] 1.361394 317
## [128] 1.369869 216
## [129] 1.053388 210
## [130] 1.056045 223
## [131] 1.098097 254
## [132] 1.021990 208
## [133] 1.367008 320
## [134] 1.370926 320
## [135] 1.541569 208
## [136] 1.391036 229
## [137] 1.083707 204
## [138] 1.045725 223
## [139] 1.020741 212
## [140] 1.044177 235
## [141] 1.115714 248
## [142] 1.048738 220
## [143] 1.401944 372
## [144] 1.423568 372
## [145] 1.529001 289
## [146] 1.383260 319
## [147] 1.495202 206
## [148] 1.386091 233
## [149] 1.565267 221
## [150] 1.375776 237
## [151] 1.508444 225
## [152] 1.395672 254
## [153] 1.566452 272
## [154] 1.392430 295
## [155] 1.530415 264
## [156] 1.396869 294
## [157] 1.510365 270
## [158] 1.380028 301
## [159] 1.525848 258
## [160] 1.396006 288
## [161] 1.535653 299
## [162] 1.372280 326
## [163] 1.566607 264
## [164] 1.390995 286
## [165] 1.496491 259
## [166] 1.354389 286
## [167] 1.411097 544
## [168] 1.553716 544
## [169] 1.082171 221
## [170] 1.058995 281
## [171] 1.083908 259
## [172] 1.100407 266
## [173] 1.114413 257
## [174] 1.117703 309
## [175] 1.039000 236
## [176] 1.041720 233
## [177] 1.389644 417
## [178] 1.400497 417
## [179] 1.032683 236
## [180] 1.085063 254
## [181] 1.032047 276
## [182] 1.046243 250
## [183] 1.075724 269
## [184] 1.104107 283
## [185] 1.065648 265
## [186] 1.031607 268
## [187] 1.394134 429
## [188] 1.408482 429
## [189] 1.042809 226
## [190] 1.043629 253
## [191] 1.043211 250
## [192] 1.065327 259
## [193] 1.091876 292
## [194] 1.129682 277
## [195] 1.040373 271
## [196] 1.399455 418
## [197] 1.409245 418
## [198] 1.018755 259
## [199] 1.045360 289
## [200] 1.029919 274
## [201] 1.057688 324
## [202] 1.103579 266
## [203] 1.390095 449
## [204] 1.352775 449
## [205] 1.035963 290
## [206] 1.054123 350
## [207] 1.146144 371
## [208] 1.057486 321
## [209] 1.397618 499
## [210] 1.391259 499
## [211] 1.051769 301
## [212] 1.049663 323
## [213] 1.137773 343
## [214] 1.038765 298
## [215] 1.397289 495
## [216] 1.375739 495
## [217] 1.057549 310
## [218] 1.093745 351
## [219] 1.042830 318
## [220] 1.398577 511
## [221] 1.404650 511
## [222] 1.144077 360
## [223] 1.089629 330
## [224] 1.039038 332
## [225] 1.393541 514
## [226] 1.365871 514
## [227] 1.093566 395
## [228] 1.061518 379
## [229] 1.406752 599
## [230] 1.417982 599
## [231] 1.399492 523
## [232] 1.379353 523
## [233] 1.394564 557
## [234] 1.419986 557
## [235] 1.398693 205
## [236] 1.417647 205
## [237] 1.418867 205
## [238] 1.572412 205
## [239] 1.412722 210
## [240] 1.487358 210
## [241] 1.404761 282
## [242] 1.552767 282
## [243] 1.394032 214
## [244] 1.586037 214
## [245] 1.426834 223
## [246] 1.542123 223
## [247] 1.402581 265
## [248] 1.577872 265
## [249] 1.412365 259
## [250] 1.547392 259
## [251] 1.428967 268
## [252] 1.563926 268
## [253] 1.411731 253
## [254] 1.543036 253
## [255] 1.410742 293
## [256] 1.578694 293
## [257] 1.412365 259
## [258] 1.590676 259
## [259] 1.417397 255
## [260] 1.566109 255
## [261] 1.396006 224
## [262] 1.400199 224
## [263] 1.398302 203
## [264] 1.376716 203
## [265] 1.394842 218
## [266] 1.439537 218
## [267] 1.396006 256
## [268] 1.455223 256
## [269] 1.375930 216
## [270] 1.374651 216
## [271] 1.398498 204
## [272] 1.433303 204
## [273] 1.393191 210
## [274] 1.371245 210
## [275] 1.408869 229
## [276] 1.421337 229
## [277] 1.392976 209
## [278] 1.385314 209
## [279] 1.386800 210
## [280] 1.376361 210
## [281] 1.398302 203
## [282] 1.409365 203
## [283] 1.419162 208
## [284] 1.410625 208
## [285] 1.409259 232
## [286] 1.395575 232
## [287] 1.401579 221
## [288] 1.401395 221
## [289] 1.394032 214
## [290] 1.387051 214
## [291] 1.392096 205
## [292] 1.423250 205
## [293] 1.407207 217
## [294] 1.318894 217
## [295] 1.400189 213
## [296] 1.365454 213
## [297] 1.398831 240
## [298] 1.301110 240
## [299] 1.405837 208
## [300] 1.373503 208
## [301] 1.386311 208
## [302] 1.343205 208
## [303] 1.378887 227
## [304] 1.408923 227
## [305] 1.395431 221
## [306] 1.352566 221
## [307] 1.414809 228
## [308] 1.380972 228
## [309] 1.150593 207
## [310] 1.403250 270
## [311] 1.355014 270
## [312] 1.151705 212
## [313] 1.415953 299
## [314] 1.415615 299
## [315] 1.401746 259
## [316] 1.417238 259
## [317] 1.394466 247
## [318] 1.399533 247
## [319] 1.393003 239
## [320] 1.394695 239
## [321] 1.388010 242
## [322] 1.316015 242
## [323] 1.414196 278
## [324] 1.423635 278
## [325] 1.402399 226
## [326] 1.332110 226
## [327] 1.394466 247
## [328] 1.386119 247
## [329] 1.398160 236
## [330] 1.337206 236
## [331] 1.414809 285
## [332] 1.426217 285
## [333] 1.412570 261
## [334] 1.441655 261
## [335] 1.401020 254
## [336] 1.448541 254
## [337] 1.405354 287
## [338] 1.400320 287
## [339] 1.401746 259
## [340] 1.378586 259
## [341] 1.402307 263
## [342] 1.391444 263
## [343] 1.030371 214
## [344] 1.400249 320
## [345] 1.422987 320
## [346] 1.403184 308
## [347] 1.427445 308
## [348] 1.394485 278
## [349] 1.379398 278insp_list## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## NULL
##
## [[4]]
## lhs rhs support confidence lift count
## [1] {experiment} => {result} 0.1078431 0.8461538 1.798077 11
## [2] {practic} => {network} 0.1078431 0.9166667 1.246667 11
## [3] {robust} => {network} 0.1176471 1.0000000 1.360000 12
## [4] {optim} => {network} 0.1078431 0.9166667 1.246667 11
## [5] {find} => {perform} 0.1176471 0.9230769 1.810651 12
## [6] {util} => {network} 0.1078431 0.9166667 1.246667 11
## [7] {order} => {network} 0.1176471 0.9230769 1.255385 12
## [8] {cnn} => {convolut} 0.1078431 0.8461538 2.213018 11
## [9] {cnn} => {neural} 0.1176471 0.9230769 1.518610 12
## [10] {cnn} => {network} 0.1176471 0.9230769 1.255385 12
##
## [[5]]
## lhs rhs support confidence lift count
## [1] {layer} => {network} 0.1181435 0.8484848 1.175970 28
## [2] {estim} => {network} 0.1054852 0.8333333 1.154971 25
## [3] {implement} => {network} 0.1054852 0.8064516 1.117714 25
## [4] {success} => {network} 0.1054852 0.7575758 1.049973 25
## [5] {experiment} => {result} 0.1139241 0.8437500 1.851562 27
## [6] {experiment} => {network} 0.1012658 0.7500000 1.039474 24
## [7] {increas} => {network} 0.1012658 0.7741935 1.073005 24
## [8] {input} => {network} 0.1223629 0.8285714 1.148371 29
## [9] {research} => {data} 0.1054852 0.7352941 1.613562 25
## [10] {order} => {network} 0.1265823 0.8333333 1.154971 30
##
## [[6]]
## lhs rhs support confidence lift count
## [1] {cnn} => {convolut} 0.1012862 0.8513514 2.674447 63
## [2] {cnn} => {neural} 0.1045016 0.8783784 1.556557 65
## [3] {cnn} => {network} 0.1125402 0.9459459 1.337224 70
## [4] {outperform} => {network} 0.1012862 0.7590361 1.073001 63
## [5] {general} => {network} 0.1045016 0.7738095 1.093885 65
## [6] {number} => {network} 0.1028939 0.7901235 1.116947 64
## [7] {input} => {network} 0.1093248 0.8500000 1.201591 68
## [8] {layer} => {network} 0.1189711 0.8809524 1.245346 74
## [9] {effici} => {network} 0.1061093 0.7415730 1.048315 66
## [10] {test} => {network} 0.1157556 0.7578947 1.071388 72
##
## [[7]]
## lhs rhs support confidence lift count
## [1] {structur} => {network} 0.1058824 0.7883212 1.161976 108
## [2] {optim} => {network} 0.1088235 0.7449664 1.098072 111
## [3] {complex} => {network} 0.1009804 0.7202797 1.061684 103
## [4] {input} => {network} 0.1137255 0.7837838 1.155288 116
## [5] {learn} => {network} 0.1215686 0.7898089 1.164169 124
## [6] {stateoftheart} => {network} 0.1205882 0.7068966 1.041957 123
## [7] {classif} => {network} 0.1333333 0.7311828 1.077755 136
## [8] {effici} => {network} 0.1294118 0.7252747 1.069047 132
## [9] {design} => {network} 0.1313725 0.7322404 1.079314 134
## [10] {appli} => {network} 0.1392157 0.7434555 1.095845 142
##
## [[8]]
## lhs rhs support confidence lift count
## [1] {cnn} => {neural} 0.1006413 0.8908297 1.564742 204
## [2] {cnn} => {network} 0.1060681 0.9388646 1.351618 215
## [3] {layer} => {network} 0.1080414 0.8390805 1.207966 219
## [4] {general} => {network} 0.1031080 0.7491039 1.078433 209
## [5] {function} => {network} 0.1006413 0.7391304 1.064075 204
## [6] {input} => {network} 0.1159349 0.8131488 1.170634 235
## [7] {complex} => {network} 0.1050814 0.7421603 1.068437 213
## [8] {number} => {network} 0.1045881 0.7138047 1.027615 212
## [9] {optim} => {network} 0.1124815 0.7549669 1.086873 228
## [10] {test} => {network} 0.1149482 0.7147239 1.028938 23315년도 분석을 보면, experiment -> result가 1위다. 즉, 실험을 통해 결과를 도출하는 방식이 많이 이루어졌다. 그 후 16년도부터 layer라는 단어가 급증했고, 17년도 부터는 CNN 기법이 대세를 이루었다.